Processing Social Media Messages in Mass Emergency: A Survey
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paper_title: Supporting the social media needs of emergency public information officers with human-centered design and development

paper_content:

Emergency response agencies, which operate as command-and-control organizations, push information to members of the public with too few mechanisms to support communication flowing back. Recently, information communication technologies (ICTs) such as social media have challenged this one-way model by allowing the public to participate in emergency response in new and unexpected ways. These developments place new pressure on emergency managers to release information over social media streams, monitor online activities during an emergency event, incorporate information provided by members of the public into response efforts, and engage in the public conversation around an event. ::: Within US emergency response organizations, public information officers (PIOs) are in a unique position to use these emerging communication technologies. PIOs are responsible for communicating official response information to members of the public during an emergency event and ensuring that the information available in the public arena is accurate and complete. ::: In this dissertation work, I examine how social media and the forms of public participation enabled by it are changing the role of the PIO. Based on this understanding, I explore ICT solutions for the PIO through human-centered methods that include the PIO in the design process. Finally, I design, implement, and evaluate a software application informed by this work that supports the social media needs of PIOs. With the aim of improving emergency response efforts, I demonstrate how empirically-based understandings of emergency management work can inform technology design, practice, and policy. ::: This dissertation research provides the following contributions: (1) an examination of PIOs' roles and the sociotechnical environment in which they work; (2) a new model of PIO communication that takes into account new communication pathways that have been enabled by ICT; (3) a set of requirements for supporting PIO social media communication needs; (4) the design, implementation, and evaluation of a tool—the PIO Monitoring Application—that supports the social media monitoring, documenting, reporting, and organizing needs of PIOs during an emergency event; (5) a description of the likely future role of PIOs and how that role might be supported.
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paper_title: Citizen communications in crisis: anticipating a future of ICT-supported public participation

paper_content:

Recent world-wide crisis events have drawn new attention to the role information communication technology (ICT) can play in warning and response activities. Drawing on disaster social science, we consider a critical aspect of post-impact disaster response that does not yet receive much information science research attention. Public participation is an emerging, large-scale arena for computer-mediated interaction that has implications for both informal and formal response. With a focus on persistent citizen communications as one form of interaction in this arena, we describe their spatial and temporal arrangements, and how the emerging information pathways that result serve different post-impact functions. However, command-and-control models do not easily adapt to the expanding data-generating and -seeking activities by the public. ICT in disaster contexts will give further rise to improvised activities and temporary organizations with which formal response organizations need to align.
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paper_title: Disasters by Design: A Reassessment of Natural Hazards in the United States

paper_content:

Summary of the book, which emphasizes on the fact that natural disasters and technological hazards that may accompany them are not problems that can be solved in isolation. Rather, they are symptoms of broader and more basic problems. Losses from hazards -and the fact that the nation cannot seem to reduce them-result from shrotsighted and narrow conceptions of the human relationship to the natural environment. To redress those shortcomings, the nation must shift to a policy of "sustainable hazard mitigation". This concept links wise management of natural resources with local economic and social resiliency, viewing hazard mitigation as an integral part of a much larger context. Many aspects of this strategy were implicit in the recommendations formulated by White and Haas a quarter-century ago. Bu to head off the continued rise in tolls from disasters, those principles must become more explicit. This summary, and the report on wich it is based, reflect the efforts of over a hundred experts who have worked and debated since 1994 to take stock of American's relationship to hazards past, present, and-most importantly-future. Those contributions have been used to outline a comprehensive approach to enhancing society's ability to reduce the costs of disaster. It briefly reefers to the roots of the problem, the fostering local sustainability, the mitigation tools, the essential steps to be followed ant the key role of the hazards community
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paper_title: A survey of trust in social networks

paper_content:

Web-based social networks have become popular as a medium for disseminating information and connecting like-minded people. The public accessibility of such networks with the ability to share opinions, thoughts, information, and experience offers great promise to enterprises and governments. In addition to individuals using such networks to connect to their friends and families, governments and enterprises have started exploiting these platforms for delivering their services to citizens and customers. However, the success of such attempts relies on the level of trust that members have with each other as well as with the service provider. Therefore, trust becomes an essential and important element of a successful social network. In this article, we present the first comprehensive review of social and computer science literature on trust in social networks. We first review the existing definitions of trust and define social trust in the context of social networks. We then discuss recent works addressing three aspects of social trust: trust information collection, trust evaluation, and trust dissemination. Finally, we compare and contrast the literature and identify areas for further research in social trust.
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paper_title: Twitter Catches The Flu: Detecting Influenza Epidemics using Twitter

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With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.
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paper_title: Does the early bird move the polls?: the use of the social media tool 'Twitter' by U.S. politicians and its impact on public opinion

paper_content:

Social media tools such as Twitter and Facebook have been hailed as politically transformative communications technologies, on the level of previous politically significant technological innovations, such as radio and television. In this paper we estimate the impact of the use of the social media tool 'Twitter' by American politicians on changes in public opinion of those politicians over time, and we find little evidence that the political use of Twitter has eitther a positive or negative impact on public opinion. We run simple regressions of changes in widely used U. S. public opinion polls, estimating the association between favorable impressions of selected politicians and the social media usage levels (number of 'Tweets') of those politicians. In order to get a reliable estimate, we ran the same regressions for public opinion polls conducted by three different companies (CNN/Opinion Research, Gallup, and ABC/Washington Post) and we find that the estimated coefficients are not significant for any of the tested opinion polls.
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paper_title: "Beacons of hope" in decentralized coordination: learning from on-the-ground medical twitterers during the 2010 Haiti earthquake

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We examine the public, social media communications of 110 emergency medical response teams and organizations in the immediate aftermath of the January 12, 2010 Haiti earthquake. We found the teams through an inductive analysis of Twitter communications acquired over the three-week emergency period from 89,114 Twitterers. We then analyzed the teams' Twitter streams, as well as all digital media they generated and pointed to in their streams - blog posts, photographs, videos, status updates and field reports - to understand the medical coordination challenges they faced from pre-deployment readiness to on-the-ground action. Here we identify opportunities for improving coordination in a decentralized and distributed environment where staffing, disease trajectories, and other circumstances rapidly change. We extrapolate from these findings to theorize about how "beaconing" behavior is a sign of latent potential for coordination upon which mechanisms of coordination can capitalize.
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paper_title: Designing for the deluge: understanding & supporting the distributed, collaborative work of crisis volunteers

paper_content:

Social media are a potentially valuable source of situational awareness information during crisis events. Consistently, "digital volunteers" and others are coming together to filter and process this data into usable resources, often coordinating their work within distributed online groups. However, current tools and practices are frequently unable to keep up with the speed and volume of incoming data during large events. Through contextual interviews with emergency response professionals and digital volunteers, this research examines the ad hoc, collaborative practices that have emerged to help process this data and outlines strategies for supporting and leveraging these efforts in future designs. We argue for solutions that align with current group values, work practices, volunteer motivations, and organizational structures, but also allow these groups to increase the scale and efficiency of their operations.
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paper_title: Community emergency planning : False assumptions and inappropriate analogies

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Community emergency planning had its roots in military analogies which viewed emergencies as extensions of "enemy attack" scenarios. Such thinking was embedded in early structural arrangements and was generalized as the appropriate normative model for all emergencies. This model viewed emergencies as conditions of social chaos which could be rectified by command and control. It is argued here that such a view is inadequate based on a knowledge of behavior in emergencies and the model is dysfunctional for planning. A more adequate model is presented, based on conditions of continuity, coordination and cooperation. This problem solving model, based on research rather than military analogies, provides a more adequate set of assumptions as the basis for planning. However, legislative and technological "improvements" often make emergency planning more rigid and increasingly (AU)
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paper_title: Catastrophe in reel life versus real life: perpetuating disaster myth through Hollywood films

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Hollywood productions continuously feature disasters and the human struggle against them. Recent entries from the disaster movie genre include films on tornadoes, volcanoes, and asteroids. This article is an exploration of how mass media, specifically the entertainment industry, conveys messages about disasters. For this paper, we examine eleven disaster films looking for five key disaster "myths" as identified by Jones (1993) that perpetuate common misconceptions about hazards. Specifically, we research whether the entertainment industry passes along these myths; we also provide an update for earlier work conducted by Quarantelli (1985) and conclude that the message regarding hazards provided through these films is often mixed and inconsistent. Hollywood's fascination with the disaster genre, the validity of the science portrayed, and the language used to characterize the disaster are among the topics we explore between "reel" life and "real" life.
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paper_title: Delivering patients to sacré coeur: collective intelligence in digital volunteer communities

paper_content:

This study examines the information-processing activities of digital volunteers and other connected ICT users in the wake of crisis events. Synthesizing findings from several previous research studies of digital volunteerism, this paper offers a new approach for conceptualizing the activities of digital volunteers, shifting from a focus on organizing to a focus on information movement. Using the lens of distributed cognition, this research describes collective intelligence as transformations of information within a system where cognition is distributed socially across individuals as well as through their tools and resources. This paper demonstrates how digital volunteers, through activities such as relaying, amplifying, verifying, and structuring information, function as a collectively intelligent cognitive system in the wake of disaster events.
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paper_title: The Evolving Role of the Public Information Officer: An Examination of Social Media in Emergency Management

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Abstract This work examines how the introduction of social media has affected the role of the Public Information Officer (PIO)—the public relations component of the National Incident Management System (NIMS). Through analysis of 25 PIO interviews, we examine the work practice of PIOs and find that social media expand not only the scope and type of PIO work activity, but also the “information pathways” that exist between PIOs, the media, and members of the public. We model these changes and examine how the presence of social media challenges previous conceptualizations of PIO work. Lastly, we present a view of how PIO work could be better imagined for the future of emergency management organizations.
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paper_title: EARS (earthquake alert and report system): a real time decision support system for earthquake crisis management

paper_content:

Social sensing is based on the idea that communities or groups of people can provide a set of information similar to those obtainable from a sensor network. Emergency management is a candidate field of application for social sensing. In this work we describe the design, implementation and deployment of a decision support system for the detection and the damage assessment of earthquakes in Italy. Our system exploits the messages shared in real-time on Twitter, one of the most popular social networks in the world. Data mining and natural language processing techniques are employed to select meaningful and comprehensive sets of tweets. We then apply a burst detection algorithm in order to promptly identify outbreaking seismic events. Detected events are automatically broadcasted by our system via a dedicated Twitter account and by email notifications. In addition, we mine the content of the messages associated to an event to discover knowledge on its consequences. Finally we compare our results with official data provided by the National Institute of Geophysics and Volcanology (INGV), the authority responsible for monitoring seismic events in Italy. The INGV network detects shaking levels produced by the earthquake, but can only model the damage scenario by using empirical relationships. This scenario can be greatly improved with direct information site by site. Results show that the system has a great ability to detect events of a magnitude in the region of 3.5, with relatively low occurrences of false positives. Earthquake detection mostly occurs within seconds of the event and far earlier than the notifications shared by INGV or by other official channels. Thus, we are able to alert interested parties promptly. Information discovered by our system can be extremely useful to all the government agencies interested in mitigating the impact of earthquakes, as well as the news agencies looking for fresh information to publish.
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paper_title: SensePlace2: GeoTwitter analytics support for situational awareness

paper_content:

Geographically-grounded situational awareness (SA) is critical to crisis management and is essential in many other decision making domains that range from infectious disease monitoring, through regional planning, to political campaigning. Social media are becoming an important information input to support situational assessment (to produce awareness) in all domains. Here, we present a geovisual analytics approach to supporting SA for crisis events using one source of social media, Twitter. Specifically, we focus on leveraging explicit and implicit geographic information for tweets, on developing place-time-theme indexing schemes that support overview+detail methods and that scale analytical capabilities to relatively large tweet volumes, and on providing visual interface methods to enable understanding of place, time, and theme components of evolving situations. Our approach is user-centered, using scenario-based design methods that include formal scenarios to guide design and validate implementation as well as a systematic claims analysis to justify design choices and provide a framework for future testing. The work is informed by a structured survey of practitioners and the end product of Phase-I development is demonstrated / validated through implementation in SensePlace2, a map-based, web application initially focused on tweets but extensible to other media.
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paper_title: LITMUS: Landslide detection by integrating multiple sources

paper_content:

Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS’ functionality can be used to determine landslides related to the recent Typhoon Haiyan.
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paper_title: Using Social Media to Enhance Emergency Situation Awareness

paper_content:

Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.
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paper_title: Emergency Situation Awareness: Twitter Case Studies

paper_content:

The Emergency Situation Awareness (ESA) system provides all-hazard situation awareness information for emergency managers using content gathered from the public Twitter API. It collects, filters and analyses Tweets from specific regions of interest in near-real-time, enabling effective alerting for unexpected incidents and monitoring of emergency events with results accessible via an interactive website.
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paper_title: AIDR: artificial intelligence for disaster response

paper_content:

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.
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paper_title: Quantifying Information Flow During Emergencies

paper_content:

Recent advances on human dynamics have focused on the normal patterns of human activities, with the quantitative understanding of human behavior under extreme events remaining a crucial missing chapter. This has a wide array of potential applications, ranging from emergency response and detection to traffic control and management. Previous studies have shown that human communications are both temporally and spatially localized following the onset of emergencies, indicating that social propagation is a primary means to propagate situational awareness. We study real anomalous events using country-wide mobile phone data, finding that information flow during emergencies is dominated by repeated communications. We further demonstrate that the observed communication patterns cannot be explained by inherent reciprocity in social networks, and are universal across different demographics.
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paper_title: Robust dynamic classes revealed by measuring the response function of a social system

paper_content:

We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48–53] and [Roehner BM, et al., (2004) Int J Mod Phys C 15:809–834], and provides a unique framework for the investigation of timing in complex systems.
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paper_title: Twitter for crisis communication: lessons learned from Japan's tsunami disaster

paper_content:

Two weeks after the Great Tohoku earthquake followed by the devastating tsunami, we have sent open-ended questionnaires to a randomly selected sample of Twitter users and also analysed the tweets sent from the disaster-hit areas. We found that people in directly affected areas tend to tweet about their unsafe and uncertain situation while people in remote areas post messages to let their followers know that they are safe. Our analysis of the open-ended answers has revealed that unreliable retweets (RTs) on Twitter was the biggest problem the users have faced during the disaster. Some of the solutions offered by the respondents included introducing official hash tags, limiting the number of RTs for each hash tag and adding features that allow users to trace information by maintaining anonymity.
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paper_title: CrisisLex: A lexicon for collecting and filtering Microblogged communications in crises

paper_content:

Keywords: Crisis Informatics ; Social Media Collection ; Social Media Analysis Reference EPFL-CONF-203561 Record created on 2014-11-26, modified on 2017-05-12
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paper_title: Practical extraction of disaster-relevant information from social media

paper_content:

During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.
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paper_title: Disasters by Design: A Reassessment of Natural Hazards in the United States

paper_content:

Summary of the book, which emphasizes on the fact that natural disasters and technological hazards that may accompany them are not problems that can be solved in isolation. Rather, they are symptoms of broader and more basic problems. Losses from hazards -and the fact that the nation cannot seem to reduce them-result from shrotsighted and narrow conceptions of the human relationship to the natural environment. To redress those shortcomings, the nation must shift to a policy of "sustainable hazard mitigation". This concept links wise management of natural resources with local economic and social resiliency, viewing hazard mitigation as an integral part of a much larger context. Many aspects of this strategy were implicit in the recommendations formulated by White and Haas a quarter-century ago. Bu to head off the continued rise in tolls from disasters, those principles must become more explicit. This summary, and the report on wich it is based, reflect the efforts of over a hundred experts who have worked and debated since 1994 to take stock of American's relationship to hazards past, present, and-most importantly-future. Those contributions have been used to outline a comprehensive approach to enhancing society's ability to reduce the costs of disaster. It briefly reefers to the roots of the problem, the fostering local sustainability, the mitigation tools, the essential steps to be followed ant the key role of the hazards community
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paper_title: CrisisLex: A lexicon for collecting and filtering Microblogged communications in crises

paper_content:

Keywords: Crisis Informatics ; Social Media Collection ; Social Media Analysis Reference EPFL-CONF-203561 Record created on 2014-11-26, modified on 2017-05-12
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paper_title: Technical Limitations for Designing Applications for Social Media

paper_content:

Summary Social media content is used in various applications for businesses, organizations and citizens. However, there are technical limitations for analyzing content from social media; these include the way how data can be gained and which safety regulations as well as query limitations have to be considered. They are of specific importance when designing applications for time critical scenarios, such as crisis management. This paper analyzes these limitations (in June 2014) for the most important social media. The selection of social media is based on the Monthly Active Users (MAU), which counts unique users over 30 days. Besides the identification of various limitations, this paper outlines approaches to access the data and summarizes design considerations.
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paper_title: Upvoting hurricane Sandy: event-based news production processes on a social news site

paper_content:

This paper uses the case of Hurricane Sandy and reddit's topical community (subreddit) /r/sandy to examine the production and curation of news content around events on a social news site. Through qualitative analysis, we provide a coded topology of produced content and describe how types of networked gatekeeping impact the framing of a crisis situation. This study also examines, through quantitative modeling, what kind of information becomes negotiated and voted as relevant. We suggest that highly scored content shared in a social news setting focused more on human-interest media and perspective-based citizen journalism than professional news reports. We conclude by discussing how the mechanisms of social news sites conflict with the social norms and culture of reddit to produce differing expectations around news.
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paper_title: Opinion mining and sentiment analysis

paper_content:

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object. ::: ::: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.
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paper_title: Improved Part-of-Speech Tagging for Online Conversational Text with Word Clusters

paper_content:

We consider the problem of part-of-speech tagging for informal, online conversational text. We systematically evaluate the use of large-scale unsupervised word clustering and new lexical features to improve tagging accuracy. With these features, our system achieves state-of-the-art tagging results on both Twitter and IRC POS tagging tasks; Twitter tagging is improved from 90% to 93% accuracy (more than 3% absolute). Qualitative analysis of these word clusters yields insights about NLP and linguistic phenomena in this genre. Additionally, we contribute the first POS annotation guidelines for such text and release a new dataset of English language tweets annotated using these guidelines. Tagging software, annotation guidelines, and large-scale word clusters are available at: http://www.ark.cs.cmu.edu/TweetNLP This paper describes release 0.3 of the “CMU Twitter Part-of-Speech Tagger” and annotated data. [This paper is forthcoming in Proceedings of NAACL 2013; Atlanta, GA, USA.]
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paper_title: Credibility ranking of tweets during high impact events

paper_content:

Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important content and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.
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paper_title: Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake

paper_content:

In this work, we conducted a case study of a popular Chinese microblogging site, Sina-Weibo, to investigate how Chinese netizens used microblogging in response to a major disaster: the 2010 Yushu Earthquake. We combined multiple analysis methods in this case study, including content analysis of microblog messages, trend analysis of different topics, and an analysis of the information spreading process. This study helped us understand the roles played by microblogging systems in response to major disasters and enabled us to gain insight into how to harness the power of microblogging to facilitate disaster response. In addition, this work supplements existing works with an exploration of a non-Western socio-cultural system: how Chinese Internet users used microblogging in disaster response.
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paper_title: CrisisLex: A lexicon for collecting and filtering Microblogged communications in crises

paper_content:

Keywords: Crisis Informatics ; Social Media Collection ; Social Media Analysis Reference EPFL-CONF-203561 Record created on 2014-11-26, modified on 2017-05-12
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paper_title: Detecting spammers on twitter

paper_content:

With millions of users tweeting around the world, real time search systems and dierent types of mining tools are emerging to allow people tracking the repercussion of events and news on Twitter. However, although appealing as mechanisms to ease the spread of news and allow users to discuss events and post their status, these services open opportunities for new forms of spam. Trending topics, the most talked about items on Twitter at a given point in time, have been seen as an opportunity to generate trac and revenue. Spammers post tweets containing typical words of a trending topic and URLs, usually obfuscated by URL shorteners, that lead users to completely unrelated websites. This kind of spam can contribute to de-value real time search services unless mechanisms to fight and stop spammers can be found. In this paper we consider the problem of detecting spammers on Twitter. We first collected a large dataset of Twitter that includes more than 54 million users, 1.9 billion links, and almost 1.8 billion tweets. Using tweets related to three famous trending topics from 2009, we construct a large labeled collection of users, manually classified into spammers and non-spammers. We then identify a number of characteristics related to tweet content and user social behavior, which could potentially be used to detect spammers. We used these characteristics as attributes of machine learning process for classifying users as either spammers or nonspammers. Our strategy succeeds at detecting much of the spammers while only a small percentage of non-spammers are misclassified. Approximately 70% of spammers and 96% of non-spammers were correctly classified. Our results also highlight the most important attributes for spam detection on Twitter.
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paper_title: Understanding Types of Users on Twitter

paper_content:

People use microblogging platforms like Twitter to involve with other users for a wide range of interests and practices. Twitter profiles run by different types of users such as humans, bots, spammers, businesses and professionals. This research work identifies six broad classes of Twitter users, and employs a supervised machine learning approach which uses a comprehensive set of features to classify users into the identified classes. For this purpose, we exploit users' profile and tweeting behavior information. We evaluate our approach by performing 10-fold cross validation using manually annotated 716 different Twitter profiles. High classification accuracy (measured using AUC, and precision, recall) reveals the significance of the proposed approach.
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paper_title: Location-based insights from the social web

paper_content:

Citizens, news reporters, relief organizations, and governments are increasingly relying on the Social Web to report on and respond to disasters as they occur. The capability to rapidly react to important events, which can be identified from high-volume streams even when the sources are unknown, still requires precise localization of the events and verification of the reports. In this paper, we propose a framework for classifying location elements and a method for their extraction from Social Web data. We describe the framework in the context of existing Social Web systems used for disaster management. We present a new location-inferencing architecture and evaluate its performance with a data set from a real-world disaster.
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paper_title: "Right Time, Right Place" Health Communication on Twitter: Value and Accuracy of Location Information

paper_content:

Background: Twitter provides various types of location data, including exact Global Positioning System (GPS) coordinates, which could be used for infoveillance and infodemiology (ie, the study and monitoring of online health information), health communication, and interventions. Despite its potential, Twitter location information is not well understood or well documented, limiting its public health utility. Objective: The objective of this study was to document and describe the various types of location information available in Twitter. The different types of location data that can be ascertained from Twitter users are described. This information is key to informing future research on the availability, usability, and limitations of such location data. Methods: Location data was gathered directly from Twitter using its application programming interface (API). The maximum tweets allowed by Twitter were gathered (1% of the total tweets) over 2 separate weeks in October and November 2011. The final dataset consisted of 23.8 million tweets from 9.5 million unique users. Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state’s population with the number of Twitter users who enable the GPS location option. Results: The GPS location data could be ascertained for 2.02% of tweets and 2.70% of unique users. Using a simple text-matching approach, 17.13% of user profiles in the 4 continental US time zones were able to be used to determine the user’s city and state. Agreement between GPS data and data from the text-matching approach was high (87.69%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r ≥ 0.97, P < .001). Conclusions: Health researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location. Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter. Given the strong correlation between both data gathering methods, future research may consider using more qualitative approaches with higher yields, such as text mining, to acquire information about Twitter users’ geographical location. [J Med Internet Res 2012;14(6):e156]
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paper_title: You are where you tweet: a content-based approach to geo-locating twitter users

paper_content:

We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues. By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on. Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. The system estimates k possible locations for each user in descending order of confidence. On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location.
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paper_title: Location extraction from disaster-related microblogs

paper_content:

Location information is critical to understanding the impact of a disaster, including where the damage is, where people need assistance and where help is available. We investigate the feasibility of applying Named Entity Recognizers to extract locations from microblogs, at the level of both geo-location and point-of-interest. Our experimental results show that such tools once retrained on microblog data have great potential to detect the where information, even at the granularity of point-of-interest.
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paper_title: Towards Understanding Global Spread of Disease from Everyday Interpersonal Interactions

paper_content:

Monitoring and forecast of global spread of infectious diseases is difficult, mainly due to lack of fine-grained and timely data. Previous work in computational epidemiology has shown that mining data from the web can improve the predictability of high-level aggregate patterns of epidemics. By contrast, this paper explores how individuals contribute to the global spread of disease. We consider the important task of predicting the prevalence of flu-like illness in a given city based on interpersonal interactions of the city's residents with the outside world. We use the geo-tagged status updates of traveling Twitter users to infer properties of the flow of individuals between cities. While previous research considered only the raw volume of passengers, we estimate a number of latent variables, including the number of sick (symptomatic) travelers and the number of sick individuals to whom each traveler was exposed. We show that AI techniques provide insights into the mechanisms of disease spread and significantly improve predictability of future flu outbreaks. Our experiments involve over 51,000 individuals traveling between 75 cities prior and during a severe ongoing flu epidemic (October 2012 - January 2013). Our model leverages the text and interpersonal interactions recorded in over 6.5 million online status updates without any active user participation, enabling scalable public health applications.
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paper_title: TREC 2013 Temporal Summarization

paper_content:

Unexpected news events such as earthquakes or natural disasters represent a unique information access problem where traditional approaches fail. For example, immediately after an event, the corpus may be sparsely populated with relevant content. Even when, after a few hours, relevant content is available, it is often inaccurate or highly redundant. At the same time, crisis events demonstrate a scenario where users urgently need information, especially if they are directly affected by the event. The goal of this track is to develop systems for efficiently monitoring the information associated with an event over time. Specifically, we are interested in developing systems which (1) can broadcast short, relevant, and reliable sentence-length updates about a developing event and (2) can track the value of important event-related attributes (e.g. number of fatalities). The track has the following goals,
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paper_title: Updating Users About Time Critical Events ⋆

paper_content:

During unexpected events such as natural disasters, individuals rely on the information generated by news outlets to form their understanding of these events. This information, while often voluminous, is frequently degraded by the inclusion of unimportant, duplicate, or wrong information. It is important to be able to present users with only the novel, important information about these events as they develop. We present the problem of updating users about time critical news events, and focus on the task of deciding which information to select for updating users as an event develops. We propose a solution to this problem which incorporates techniques from information retrieval and multi-document summarization and evaluate this approach on a set of historic events using a large stream of news documents. We also introduce an evaluation method which is significantly less expensive than traditional approaches to temporal summarization.
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paper_title: Engineering Crowdsourced Stream Processing Systems

paper_content:

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort.
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paper_title: Rethinking Context: Leveraging Human and Machine Computation in Disaster Response

paper_content:

Human-computer systems that treat context simply as enumerated facts, rules, or axioms about the surrounding physical and social environment will always have trouble handling information requiring human pragmatic interpretation. One way to overcome such limitations is to draw upon human pragmatic awareness to create hybrid systems capable of both extracting large quantities of data and processing that data in a way that is meaningful to users. The Web extra at http://youtu.be/pqI2qcigiCw is a video demonstrating Artificial Intelligence for Disaster Response (AIDR), a free, open source, and easy-to-use platform to filter and classify relevant microblog messages during humanitarian crises.
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paper_title: A System for new event detection

paper_content:

We present a new method and system for performing the New Event Detection task, i.e., in one or multiple streams of news stories, all stories on a previously unseen (new) event are marked. The method is based on an incremental TF-IDF model. Our extensions include: generation of source-specific models, similarity score normalization based on document-specific averages, similarity score normalization based on source-pair specific averages, term reweighting based on inverse event frequencies, and segmentation of the documents. We also report on extensions that did not improve results. The system performs very well on TDT3 and TDT4 test data and scored second in the TDT-2002 evaluation.
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paper_title: 'Beating the news' with EMBERS: forecasting civil unrest using open source indicators

paper_content:

We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the June 2013 protests in Brazil and Feb 2014 violent protests in Venezuela. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
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paper_title: LeadLine: Interactive visual analysis of text data through event identification and exploration

paper_content:

Text data such as online news and microblogs bear valuable insights regarding important events and responses to such events. Events are inherently temporal, evolving over time. Existing visual text analysis systems have provided temporal views of changes based on topical themes extracted from text data. But few have associated topical themes with events that cause the changes. In this paper, we propose an interactive visual analytics system, LeadLine, to automatically identify meaningful events in news and social media data and support exploration of the events. To characterize events, LeadLine integrates topic modeling, event detection, and named entity recognition techniques to automatically extract information regarding the investigative 4 Ws: who, what, when, and where for each event. To further support analysis of the text corpora through events, LeadLine allows users to interactively examine meaningful events using the 4 Ws to develop an understanding of how and why. Through representing large-scale text corpora in the form of meaningful events, LeadLine provides a concise summary of the corpora. LeadLine also supports the construction of simple narratives through the exploration of events. To demonstrate the efficacy of LeadLine in identifying events and supporting exploration, two case studies were conducted using news and social media data.
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paper_title: Topic detection and tracking: event-based information organization

paper_content:

Topic Detection and Tracking: Event-based Information Organization brings together in one place state-of-the-art research in Topic Detection and Tracking (TDT). This collection of technical papers from leading researchers in the field not only provides several chapters devoted to the research program and its evaluation paradigm, but also presents the most current research results and describes some of the remaining open challenges. Topic Detection and Tracking: Event-based Information Organization is an excellent reference for researchers and practitioners in a variety of fields related to TDT, including information retrieval, automatic speech recognition, machine learning, and information extraction
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paper_title: A study of retrospective and on-line event detection

paper_content:

This paper investigates the use and extension ::: of text retrieval and clustering techniques for event ::: detection. The task is to automatically detect novel ::: events from a temporally-ordered stream of news stories, ::: either retrospectively or as the stories arrive. We applied ::: hierarchical and non-hierarchical document clustering algorithms ::: to a corpus of 15,836 stories, focusing on the ::: exploitation of both content and temporal information. ::: We found the resulting cluster hierarchies highly informative ::: for retrospective detection of previously unidentified ::: events, effectively supporting both query-free and ::: query-driven retrieval. We also found that temporal distribution ::: patterns of document clusters provide useful ::: information for improvement in both retrospective detection ::: and on-line detection of novel events. In an ::: evaluation using manually labelled events to judge the ::: system-detected events, we obtained a result of 82% in ::: the Fl measure for retrospective detection, and a Fl ::: value of 42% for on-line detection.
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paper_title: Twevent: segment-based event detection from tweets

paper_content:

Event detection from tweets is an important task to understand the current events/topics attracting a large number of common users. However, the unique characteristics of tweets (e.g. short and noisy content, diverse and fast changing topics, and large data volume) make event detection a challenging task. Most existing techniques proposed for well written documents (e.g. news articles) cannot be directly adopted. In this paper, we propose a segment-based event detection system for tweets, called Twevent. Twevent first detects bursty tweet segments as event segments and then clusters the event segments into events considering both their frequency distribution and content similarity. More specifically, each tweet is split into non-overlapping segments (i.e. phrases possibly refer to named entities or semantically meaningful information units). The bursty segments are identified within a fixed time window based on their frequency patterns, and each bursty segment is described by the set of tweets containing the segment published within that time window. The similarity between a pair of bursty segments is computed using their associated tweets. After clustering bursty segments into candidate events, Wikipedia is exploited to identify the realistic events and to derive the most newsworthy segments to describe the identified events. We evaluate Twevent and compare it with the state-of-the-art method using 4.3 million tweets published by Singapore-based users in June 2010. In our experiments, Twevent outperforms the state-of-the-art method by a large margin in terms of both precision and recall. More importantly, the events detected by Twevent can be easily interpreted with little background knowledge because of the newsworthy segments. We also show that Twevent is efficient and scalable, leading to a desirable solution for event detection from tweets.
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paper_title: Event detection from flickr data through wavelet-based spatial analysis

paper_content:

Detecting events from web resources has attracted increasing research interests in recent years. Our focus in this paper is to detect events from photos on Flickr, an Internet image community website. The results can be used to facilitate user searching and browsing photos by events. The problem is challenging considering: (1) Flickr data is noisy, because there are photos unrelated to real-world events; (2) It is not easy to capture the content of photos. This paper presents our effort in detecting events from Flickr photos by exploiting the tags supplied by users to annotate photos. In particular, the temporal and locational distributions of tag usage are analyzed in the first place, where a wavelet transform is employed to suppress noise. Then, we identify tags related with events, and further distinguish between tags of aperiodic events and those of periodic events. Afterwards, event-related tags are clustered such that each cluster, representing an event, consists of tags with similar temporal and locational distribution patterns as well as with similar associated photos. Finally, for each tag cluster, photos corresponding to the represented event are extracted. We evaluate the performance of our approach using a set of real data collected from Flickr. The experimental results demonstrate that our approach is effective in detecting events from the Flickr photo collection.
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paper_title: Temporal and information flow based event detection from social text streams

paper_content:

Recently, social text streams (e.g., blogs, web forums, and emails) have become ubiquitous with the evolution of the web. In some sense, social text streams are sensors of the real world. Often, it is desirable to extract real world events from the social text streams. However, existing event detection research mainly focused only on the stream properties of social text streams but ignored the contextual, temporal, and social information embedded in the streams. In this paper, we propose to detect events from social text streams by exploring the content as well as the temporal, and social dimensions. We define the term event as the information flow between a group of social actors on a specific topic over a certain time period. We represent social text streams as multi-graphs, where each node represents a social actor and each edge represents the information flow between two actors. The content and temporal associations within the flow of information are embedded in the corresponding edge. Events are detected by combining text based clustering, temporal segmentation, and information flow-based graph cuts of the dual graph of the social networks. Experiments conducted with the Enron email dataset and the political blog dataset from Dailykos show the proposed event detection approach outperforms the other alternatives.
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paper_title: Discovering Event Evolution Graphs From News Corpora

paper_content:

Given the advance of Internet technologies, we can now easily extract hundreds or thousands of news stories of any ongoing incidents from newswires such as CNN.com, but the volume of information is too large for us to capture the blueprint. Information retrieval techniques such as topic detection and tracking are able to organize news stories as events, in a flat hierarchical structure, within a topic. However, they are incapable of presenting the complex evolution relationships between the events. We are interested to learn not only what the major events are but also how they develop within the topic. It is beneficial to identify the seminal events, the intermediary and ending events, and the evolution of these events. In this paper, we propose to utilize the event timestamp, event content similarity, temporal proximity, and document distributional proximity to model the event evolution relationships between events in an incident. An event evolution graph is constructed to present the underlying structure of events for efficient browsing and extracting of information. Case study and experiments are presented to illustrate and show the performance of our proposed technique. It is found that our proposed technique outperforms the baseline technique and other comparable techniques in previous work.
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paper_title: Event detection and tracking in social streams

paper_content:

Events and stories can be characterized by a set of de- scriptive, collocated keywords. Intuitively, documents describing the same event will contain similar sets of keywords, and the graph of keywords for a document collection will contain clusters individual events. In this paper we build a network of keywords based on their co-occurrence in documents. We propose and develop a new event detection algorithm which creates a keyword graph and uses community detection methods analo- gous to those used for social network analysis to dis- cover and describe events. Constellations of keywords describing an event may be used to find related articles. We also use the proposed algorithm to analyze events and track stories in social streams.
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paper_title: Named Entity Recognition in Tweets: An Experimental Study

paper_content:

People tweet more than 100 Million times daily, yielding a noisy, informal, but sometimes informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented manner. The performance of standard NLP tools is severely degraded on tweets. This paper addresses this issue by re-building the NLP pipeline beginning with part-of-speech tagging, through chunking, to named-entity recognition. Our novel T-ner system doubles F1 score compared with the Stanford NER system. T-ner leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision. LabeledLDA outperforms co-training, increasing F1 by 25% over ten common entity types. ::: ::: Our NLP tools are available at: http://github.com/aritter/twitter_nlp
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paper_title: Automatic Identification of Crisis-Related Sub-events Using Clustering

paper_content:

Social media are becoming an important instrument for supporting crisis management, due to their broad acceptance and the intensive usage of mobile devices for accessing them. Social platforms facilitate collaboration among the public during a crisis and also support after-the-fact analysis. Thus, social media are useful for the processes of understanding, learning, and decision making. In particular, having information from social networks in a suitable, ideally summarized, form can speed up such processes. The present study relies on Flickr and You Tube as social media and aims at automatically identifying individual sub-events within a crisis situation. The study applies a two-phase clustering approach to detect those sub-events. The first phase uses geo-referenced data to locate a sub-event, while the second phase uses the natural language descriptions of pictures and videos to further identify the "what-about" of those sub-events. The results show high potential of this social media-based clustering approach for detecting crisis-related sub-events.
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paper_title: Open domain event extraction from twitter

paper_content:

Tweets are the most up-to-date and inclusive stream of in- formation and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events. Previous work on extracting structured representations of events has focused largely on newswire text; Twitter's unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal-- the first open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of significant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models. By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar.com; Our NLP tools are available at http://github.com/aritter/ twitter_nlp.
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paper_title: TwitterMonitor: trend detection over the twitter stream

paper_content:

We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend. We discuss the motivation for trend detection over social media streams and the challenges that lie therein. We then describe our approach to trend detection, as well as the architecture of TwitterMonitor. Finally, we lay out our demonstration scenario.
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paper_title: Emergency Situation Awareness: Twitter Case Studies

paper_content:

The Emergency Situation Awareness (ESA) system provides all-hazard situation awareness information for emergency managers using content gathered from the public Twitter API. It collects, filters and analyses Tweets from specific regions of interest in near-real-time, enabling effective alerting for unexpected incidents and monitoring of emergency events with results accessible via an interactive website.
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paper_title: A sensitive Twitter earthquake detector

paper_content:

This paper describes early work at developing an earthquake detector for Australia and New Zealand using Twitter. The system is based on the Emergency Situation Awareness (ESA) platform which provides all-hazard information captured, filtered and analysed from Twitter. The detector sends email notifications of evidence of earthquakes from Tweets to the Joint Australian Tsunami Warning Centre. The earthquake detector uses the ESA platform to monitor Tweets and checks for specific earthquake related alerts. The Tweets that contribute to an alert are then examined to determine their locations: when the Tweets are identified as being geographically close and the retweet percentage is low an email notification is generated. The earthquake detector has been in operation since December 2012 with 31 notifications generated where 17 corresponded with real, although minor, earthquake events. The remaining 14 were a result of discussions about earthquakes but not prompted by an event. A simple modification to our algorithm results in 20 notifications identifying the same 17 real events and reducing the false positives to 3. Our detector is sensitive in that it can generate alerts from only a few Tweets when they are determined to be geographically close.
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paper_title: Streaming First Story Detection with application to Twitter

paper_content:

With the recent rise in popularity and size of social media, there is a growing need for systems that can extract useful information from this amount of data. We address the problem of detecting new events from a stream of Twitter posts. To make event detection feasible on web-scale corpora, we present an algorithm based on locality-sensitive hashing which is able overcome the limitations of traditional approaches, while maintaining competitive results. In particular, a comparison with a state-of-the-art system on the first story detection task shows that we achieve over an order of magnitude speedup in processing time, while retaining comparable performance. Event detection experiments on a collection of 160 million Twitter posts show that celebrity deaths are the fastest spreading news on Twitter.
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paper_title: Twitinfo: aggregating and visualizing microblogs for event exploration

paper_content:

Microblogs are a tremendous repository of user-generated content about world events. However, for people trying to understand events by querying services like Twitter, a chronological log of posts makes it very difficult to get a detailed understanding of an event. In this paper, we present TwitInfo, a system for visualizing and summarizing events on Twitter. TwitInfo allows users to browse a large collection of tweets using a timeline-based display that highlights peaks of high tweet activity. A novel streaming algorithm automatically discovers these peaks and labels them meaningfully using text from the tweets. Users can drill down to subevents, and explore further via geolocation, sentiment, and popular URLs. We contribute a recall-normalized aggregate sentiment visualization to produce more honest sentiment overviews. An evaluation of the system revealed that users were able to reconstruct meaningful summaries of events in a small amount of time. An interview with a Pulitzer Prize-winning journalist suggested that the system would be especially useful for understanding a long-running event and for identifying eyewitnesses. Quantitatively, our system can identify 80-100% of manually labeled peaks, facilitating a relatively complete view of each event studied.
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paper_title: Twitter earthquake detection: earthquake monitoring in a social world

paper_content:

The U.S. Geological Survey (USGS) is investigating how the social networking site Twitter, a popular service for sending and receiving short, public text messages, can augment USGS earthquake response products and the delivery of hazard information. Rapid detection and qualitative assessment of shaking events are possible because people begin sending public Twitter messages (tweets) with in tens of seconds after feeling shaking. Here we present and evaluate an earthquake detection procedure that relies solely on Twitter data. A tweet-frequency time series constructed from tweets containing the word “earthquake” clearly shows large peaks correlated with the origin times of widely felt events. To identify possible earthquakes, we use a short-term-average, long-term-average algorithm. When tuned to a moderate sensitivity, the detector finds 48 globally-distributed earthquakes with only two false triggers in five months of data. The number of detections is small compared to the 5,175 earthquakes in the USGS global earthquake catalog for the same five-month time period, and no accurate location or magnitude can be assigned based on tweet data alone. However, Twitter earthquake detections are not without merit. The detections are generally caused by widely felt events that are of more immediate interest than those with no human impact. The detections are also fast; about 75% occur within two minutes of the origin time. This is considerably faster than seismographic detections in poorly instrumented regions of the world. The tweets triggering the detections also provided very short first-impression narratives from people who experienced the shaking.
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paper_title: What's on the grapevine?

paper_content:

User generated content and social media (in the form of blogs, wikis, online video, microblogs, etc) are proliferating online. Grapevine conducts large scale data analysis on the social media collective, distilling and extracting information in real time. It aims to track entities and stories of interest in millions of blog posts, thousands of tweets, news items, etc., daily. Grapevine facilitates the interactive exploration of content, allowing users to discover interesting or surprising stories, optionally narrowed down on a specific demographic of interest (e.g. "What are Torontonians talking about on blogs?", "What are popular stories across news sources in Canada?", "What are financiers in Texas blogging about today?"). Stories of interest can be explored in a variety of ways, such as modifying their scope, obtaining related content (blog posts, news, etc), and examining their temporal evolution.
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paper_title: Building a large-scale corpus for evaluating event detection on twitter

paper_content:

Despite the popularity of Twitter for research, there are very few publicly available corpora, and those which are available are either too small or unsuitable for tasks such as event detection. This is partially due to a number of issues associated with the creation of Twitter corpora, including restrictions on the distribution of the tweets and the difficultly of creating relevance judgements at such a large scale. The difficulty of creating relevance judgements for the task of event detection is further hampered by ambiguity in the definition of event. In this paper, we propose a methodology for the creation of an event detection corpus. Specifically, we first create a new corpus that covers a period of 4 weeks and contains over 120 million tweets, which we make available for research. We then propose a definition of event which fits the characteristics of Twitter, and using this definition, we generate a set of relevance judgements aimed specifically at the task of event detection. To do so, we make use of existing state-of-the-art event detection approaches and Wikipedia to generate a set of candidate events with associated tweets. We then use crowdsourcing to gather relevance judgements, and discuss the quality of results, including how we ensured integrity and prevented spam. As a result of this process, along with our Twitter corpus, we release relevance judgements containing over 150,000 tweets, covering more than 500 events, which can be used for the evaluation of event detection approaches.
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paper_title: Earthquake shakes Twitter users: real-time event detection by social sensors

paper_content:

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
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paper_title: LITMUS: Landslide detection by integrating multiple sources

paper_content:

Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS’ functionality can be used to determine landslides related to the recent Typhoon Haiyan.
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paper_title: SociAL Sensor Analytics: Measuring phenomenology at scale

paper_content:

The objective of this paper is to present a system for interrogating immense social media streams through analytical methodologies that characterize topics and events critical to tactical and strategic planning. First, we propose a conceptual framework for interpreting social media as a sensor network. Time-series models and topic clustering algorithms are used to implement this concept into a functioning analytical system. Next, we address two scientific challenges: 1) to understand, quantify, and baseline phenomenology of social media at scale, and 2) to develop analytical methodologies to detect and investigate events of interest. This paper then documents computational methods and reports experimental findings that address these challenges. Ultimately, the ability to process billions of social media posts per week over a period of years enables the identification of patterns and predictors of tactical and strategic concerns at an unprecedented rate through SociAL Sensor Analytics (SALSA).
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paper_title: Breaking News Detection and Tracking in Twitter

paper_content:

Twitter has been used as one of the communication channels for spreading breaking news. We propose a method to collect, group, rank and track breaking news in Twitter. Since short length messages make similarity comparison difficult, we boost scores on proper nouns to improve the grouping results. Each group is ranked based on popularity and reliability factors. Current detection method is limited to facts part of messages. We developed an application called “Hotstream” based on the proposed method. Users can discover breaking news from the Twitter timeline. Each story is provided with the information of message originator, story development and activity chart. This provides a convenient way for people to follow breaking news and stay informed with real-time updates.
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paper_title: Enhancing Event Descriptions through Twitter Mining

paper_content:

We describe a simple IR approach for linking news about events, detected by an event extraction system, to messages from Twitter (tweets). In particular, we explore several methods for creating event-specific queries for Twitter and provide a quantitative and qualitative evaluation of the relevance and usefulness of the information obtained from the tweets. We showed that methods based on utilization of word co-occurrence clustering, domain-specific keywords and named entity recognition improve the performance with respect to a basic approach.
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paper_title: Event Detection in Twitter ∗

paper_content:

Twitter, as a form of social media, is fast emerging in recent years. Users are using Twitter to report real-life events. This paper focuses on detecting those events by analyzing the text stream in Twitter. Although event detection has long been a research topic, the characteristics of Twitter make it a non-trivial task. Tweets reporting such events are usually overwhelmed by high flood of meaningless “babbles”. Moreover, event detection algorithm needs to be scalable given the sheer amount of tweets. This paper attempts to tackle these challenges with EDCoW (Event Detection with Clustering of Wavelet-based Signals). EDCoW builds signals for individual words by applying wavelet analysis on the frequencybased raw signals of the words. It then filters away the trivial words by looking at their corresponding signal autocorrelations. The remaining words are then clustered to form events with a modularity-based graph partitioning technique. Experimental results show promising result of EDCoW.
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paper_title: Beyond Trending Topics: Real-World Event Identification on Twitter

paper_content:

User-contributed messages on social media sites such as Twitter have emerged aspowerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events andnon-event messages. Our approach relies on a rich family of aggregatestatistics of topically similar message clusters. Large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter.
---
paper_title: LeadLine: Interactive visual analysis of text data through event identification and exploration

paper_content:

Text data such as online news and microblogs bear valuable insights regarding important events and responses to such events. Events are inherently temporal, evolving over time. Existing visual text analysis systems have provided temporal views of changes based on topical themes extracted from text data. But few have associated topical themes with events that cause the changes. In this paper, we propose an interactive visual analytics system, LeadLine, to automatically identify meaningful events in news and social media data and support exploration of the events. To characterize events, LeadLine integrates topic modeling, event detection, and named entity recognition techniques to automatically extract information regarding the investigative 4 Ws: who, what, when, and where for each event. To further support analysis of the text corpora through events, LeadLine allows users to interactively examine meaningful events using the 4 Ws to develop an understanding of how and why. Through representing large-scale text corpora in the form of meaningful events, LeadLine provides a concise summary of the corpora. LeadLine also supports the construction of simple narratives through the exploration of events. To demonstrate the efficacy of LeadLine in identifying events and supporting exploration, two case studies were conducted using news and social media data.
---
paper_title: Event Evolution Tracking from Streaming Social Posts

paper_content:

Online social post streams such as Twitter timelines and forum discussions have emerged as important channels for information dissemination. They are noisy, informal, and surge quickly. Real life events, which may happen and evolve every minute, are perceived and circulated in post streams by social users. Intuitively, an event can be viewed as a dense cluster of posts with a life cycle sharing the same descriptive words. There are many previous works on event detection from social streams. However, there has been surprisingly little work on tracking the evolution patterns of events, e.g., birth/death, growth/decay, merge/split, which we address in this paper. To define a tracking scope, we use a sliding time window, where old posts disappear and new posts appear at each moment. Following that, we model a social post stream as an evolving network, where each social post is a node, and edges between posts are constructed when the post similarity is above a threshold. We propose a framework which summarizes the information in the stream within the current time window as a ``sketch graph'' composed of ``core'' posts. We develop incremental update algorithms to handle highly dynamic social streams and track event evolution patterns in real time. Moreover, we visualize events as word clouds to aid human perception. Our evaluation on a real data set consisting of 5.2 million posts demonstrates that our method can effectively track event dynamics in the whole life cycle from very large volumes of social streams on the fly.
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paper_title: Topic detection and tracking: event-based information organization

paper_content:

Topic Detection and Tracking: Event-based Information Organization brings together in one place state-of-the-art research in Topic Detection and Tracking (TDT). This collection of technical papers from leading researchers in the field not only provides several chapters devoted to the research program and its evaluation paradigm, but also presents the most current research results and describes some of the remaining open challenges. Topic Detection and Tracking: Event-based Information Organization is an excellent reference for researchers and practitioners in a variety of fields related to TDT, including information retrieval, automatic speech recognition, machine learning, and information extraction
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paper_title: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data

paper_content:

We present conditional random fields , a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions made in those models. Conditional random fields also avoid a fundamental limitation of maximum entropy Markov models (MEMMs) and other discriminative Markov models based on directed graphical models, which can be biased towards states with few successor states. We present iterative parameter estimation algorithms for conditional random fields and compare the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
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paper_title: Extraction and Compilation of Events and Sub-events from Twitter

paper_content:

Twitter has emerged as a great source to provide insights about upcoming planned and unplanned events of social, economic and political relevance. Big events are publicized and known in advance, but smaller, unplanned sub-events around them are not always advertised. These unplanned events may have a large localized impact. If known in advance, knowledge about events like threats, protests, demonstrations etc. or even about large flash mobs can be utilized by planners and event managers. Given the large volumes of tweets floating around at any given time, identifying relevant sub-events is a non-trivial task. In this paper, we explore machine learning techniques to identify, extract and build a map of small sub-events around a big, popular event. We use CRFs to extract event components from tweets. Events are resolved for uniqueness and compiled into a complete calendar. The model is evaluated on tweets around Olympic Games. The framework is generic enough to be adapted to other domains.
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paper_title: Tweet4act: Using incident-specific profiles for classifying crisis-related messages

paper_content:

We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods.
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paper_title: Automatic Identification of Crisis-Related Sub-events Using Clustering

paper_content:

Social media are becoming an important instrument for supporting crisis management, due to their broad acceptance and the intensive usage of mobile devices for accessing them. Social platforms facilitate collaboration among the public during a crisis and also support after-the-fact analysis. Thus, social media are useful for the processes of understanding, learning, and decision making. In particular, having information from social networks in a suitable, ideally summarized, form can speed up such processes. The present study relies on Flickr and You Tube as social media and aims at automatically identifying individual sub-events within a crisis situation. The study applies a two-phase clustering approach to detect those sub-events. The first phase uses geo-referenced data to locate a sub-event, while the second phase uses the natural language descriptions of pictures and videos to further identify the "what-about" of those sub-events. The results show high potential of this social media-based clustering approach for detecting crisis-related sub-events.
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paper_title: STED: semi-supervised targeted-interest event detectionin in twitter

paper_content:

Social microblogs such as Twitter and Weibo are experiencing an explosive growth with billions of global users sharing their daily observations and thoughts. Beyond public interests (e.g., sports, music), microblogs can provide highly detailed information for those interested in public health, homeland security, and financial analysis. However, the language used in Twitter is heavily informal, ungrammatical, and dynamic. Existing data mining algorithms require extensive manually labeling to build and maintain a supervised system. This paper presents STED, a semi-supervised system that helps users to automatically detect and interactively visualize events of a targeted type from twitter, such as crimes, civil unrests, and disease outbreaks. Our model first applies transfer learning and label propagation to automatically generate labeled data, then learns a customized text classifier based on mini-clustering, and finally applies fast spatial scan statistics to estimate the locations of events. We demonstrate STED's usage and benefits using twitter data collected from Latin America countries, and show how our system helps to detect and track example events such as civil unrests and crimes.
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paper_title: Twitter for crisis communication: lessons learned from Japan's tsunami disaster

paper_content:

Two weeks after the Great Tohoku earthquake followed by the devastating tsunami, we have sent open-ended questionnaires to a randomly selected sample of Twitter users and also analysed the tweets sent from the disaster-hit areas. We found that people in directly affected areas tend to tweet about their unsafe and uncertain situation while people in remote areas post messages to let their followers know that they are safe. Our analysis of the open-ended answers has revealed that unreliable retweets (RTs) on Twitter was the biggest problem the users have faced during the disaster. Some of the solutions offered by the respondents included introducing official hash tags, limiting the number of RTs for each hash tag and adding features that allow users to trace information by maintaining anonymity.
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paper_title: Text Mining: Classification, Clustering, and Applications

paper_content:

Giving a broad perspective of the field from numerous vantage points, Text Mining focuses on statistical methods for text mining and analysis. It examines methods to automatically cluster and classify text documents and applies these methods in a variety of areas, including adaptive information filtering, information distillation, and text search. The book begins with the classification of documents into predefined categories and then describes novel methods for clustering documents into groups that are not predefined. It concludes with various text mining applications that have significant implications for future research and industrial use.
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paper_title: Whom should I follow?: identifying relevant users during crises

paper_content:

Social media is gaining popularity as a medium of communication before, during, and after crises. In several recent disasters, it has become evident that social media sites like Twitter and Facebook are an important source of information, and in cases they have even assisted in relief efforts. We propose a novel approach to identify a subset of active users during a crisis who can be tracked for fast access to information. Using a Twitter dataset that consists of 12.9 million tweets from 5 countries that are part of the "Arab Spring" movement, we show how instant information access can be achieved by user identification along two dimensions: user's location and the user's affinity towards topics of discussion. Through evaluations, we demonstrate that users selected by our approach generate more information and the quality of the information is better than that of users identified using state-of-the-art techniques.
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paper_title: Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake

paper_content:

In this work, we conducted a case study of a popular Chinese microblogging site, Sina-Weibo, to investigate how Chinese netizens used microblogging in response to a major disaster: the 2010 Yushu Earthquake. We combined multiple analysis methods in this case study, including content analysis of microblog messages, trend analysis of different topics, and an analysis of the information spreading process. This study helped us understand the roles played by microblogging systems in response to major disasters and enabled us to gain insight into how to harness the power of microblogging to facilitate disaster response. In addition, this work supplements existing works with an exploration of a non-Western socio-cultural system: how Chinese Internet users used microblogging in disaster response.
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paper_title: CrisisLex: A lexicon for collecting and filtering Microblogged communications in crises

paper_content:

Keywords: Crisis Informatics ; Social Media Collection ; Social Media Analysis Reference EPFL-CONF-203561 Record created on 2014-11-26, modified on 2017-05-12
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paper_title: Using Social Media to Enhance Emergency Situation Awareness

paper_content:

Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.
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paper_title: What to Expect When the Unexpected Happens: Social Media Communications Across Crises

paper_content:

The use of social media to communicate timely information during crisis situations has become a common practice in recent years. In particular, the one-to-many nature of Twitter has created an opportunity for stakeholders to disseminate crisis-relevant messages, and to access vast amounts of information they may not otherwise have. Our goal is to understand what affected populations, response agencies and other stakeholders can expect-and not expect-from these data in various types of disaster situations. Anecdotal evidence suggests that different types of crises elicit different reactions from Twitter users, but we have yet to see whether this is in fact the case. In this paper, we investigate several crises-including natural hazards and human-induced disasters-in a systematic manner and with a consistent methodology. This leads to insights about the prevalence of different information types and sources across a variety of crisis situations.
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paper_title: AIDR: artificial intelligence for disaster response

paper_content:

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.
---
paper_title: The rise and the fall of a citizen reporter

paper_content:

Recently, there has been some research interest in the development of online communities sharing news and information curated by "citizen reporters". Using "Big Data" techniques researchers try to discover influence groups and some of the major events in the lives of such communities. However, the big picture may sometimes miss important stories that are essential to the development and evolution of online communities. In particular, how does one identify and verify events when the important actors are operating anonymously and without sufficient news coverage due to safety concerns, as in drug war-torn Mexico? In this paper we present some techniques that allow us to make sense of the data collected, identify important dates of significant events in them, and direct our limited resources to discover hidden stories that, in our case, affect the lives and safety of prominent citizen reporters. In particular, we describe how focused analysis enabled us to discover an important story in the life of this community involving the reputation of an anonymous leader, and how trust was built in order to verify the validity of the story.
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paper_title: Coordinating human and machine intelligence to classify microblog communications in crises

paper_content:

An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowdsourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on realworld datasets.
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paper_title: Upvoting hurricane Sandy: event-based news production processes on a social news site

paper_content:

This paper uses the case of Hurricane Sandy and reddit's topical community (subreddit) /r/sandy to examine the production and curation of news content around events on a social news site. Through qualitative analysis, we provide a coded topology of produced content and describe how types of networked gatekeeping impact the framing of a crisis situation. This study also examines, through quantitative modeling, what kind of information becomes negotiated and voted as relevant. We suggest that highly scored content shared in a social news setting focused more on human-interest media and perspective-based citizen journalism than professional news reports. We conclude by discussing how the mechanisms of social news sites conflict with the social norms and culture of reddit to produce differing expectations around news.
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paper_title: Extracting information nuggets from disaster- Related messages in social media

paper_content:

Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
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paper_title: Online public communications by police & fire services during the 2012 Hurricane Sandy

paper_content:

Social media and other online communication tools are a subject of great interest in mass emergency response. Members of the public are turning to these solutions to seek and offer emergency information. Emergency responders are working to determine what social media policies should be in terms of their "public information" functions. We report on the online communications from all the coastal fire and police departments within a 100 mile radius of Hurricane Sandy's US landfall. Across four types of online communication media, we collected data from 840 fire and police departments. Findings indicate that few departments used these online channels in their Sandy response efforts, and that communications differed between fire and police departments and across media type. However, among the highly engaged departments, there is evidence that they bend and adapt policies about what constitutes appropriate public communication in the face of emergency demands; therefore, we propose that flexibility is important in considering future emergency online communication policy. We conclude with design recommendations for making online communication media more "listenable" for both emergency managers and members of the public.
---
paper_title: Credibility ranking of tweets during high impact events

paper_content:

Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important content and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.
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paper_title: Finding Credible Information Sources in Social Networks Based on Content and Social Structure

paper_content:

A task of primary importance for social network users is to decide whose updates to subscribe to in order to maximize the relevance, credibility, and quality of the information received. To address this problem, we conducted an experiment designed to measure the extent to which different factors in online social networks affect both explicit and implicit judgments of credibility. The results of the study indicate that both the topical content of information sources and social network structure affect source credibility. Based on these results, we designed a novel method of automatically identifying and ranking social network users according to their relevance and expertise for a given topic. We performed empirical studies to compare a variety of alternative ranking algorithms and a proprietary service provided by a commercial website specifically designed for the same purpose. Our findings show a great potential for automatically identifying and ranking credible users for any given topic.
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paper_title: Content-Based Prediction of Temporal Boundaries for Events in Twitter

paper_content:

Social media services like Twitter, Flickr and You Tube publish high volumes of user generated content as a major event occurs, making them a potential data source for event analysis. The large volume and noisy content of social media makes automatic preprocessing essential. Intuitively, the event-related data falls into three major phases: the buildup to the event, the event itself, and the post-event effects and repercussions. We describe an approach to automatically determine when an anticipated event started and ended by analyzing the content of tweets using an SVM classifier and hidden Markov model. We evaluate our performance by predicting event boundaries on Twitter data for a set of events in the domains of sports, weather and social activities.
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paper_title: Emergency management : A challenge for public administration

paper_content:

Throughout history public policy makers have sought to anticipate the unexpected in order to reduce the risk to human life and safety posed by intermittently occurring natural and man-made hazardous events. Their efforts have provided the foundation for the current focus on emergency management as an important function of federal, state, and local governments. Within the context of the various statutes, regulations, and ordinances, emergency management can be defined as the process of developing and implementing policies that are concerned with:
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paper_title: Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy

paper_content:

In today's world, online social media plays a vital role during real world events, especially crisis events. There are both positive and negative effects of social media coverage of events, it can be used by authorities for effective disaster management or by malicious entities to spread rumors and fake news. The aim of this paper, is to highlight the role of Twitter, during Hurricane Sandy (2012) to spread fake images about the disaster. We identified 10,350 unique tweets containing fake images that were circulated on Twitter, during Hurricane Sandy. We performed a characterization analysis, to understand the temporal, social reputation and influence patterns for the spread of fake images. Eighty six percent of tweets spreading the fake images were retweets, hence very few were original tweets. Our results showed that top thirty users out of 10,215 users (0.3%) resulted in 90% of the retweets of fake images; also network links such as follower relationships of Twitter, contributed very less (only 11%) to the spread of these fake photos URLs. Next, we used classification models, to distinguish fake images from real images of Hurricane Sandy. Best results were obtained from Decision Tree classifier, we got 97% accuracy in predicting fake images from real. Also, tweet based features were very effective in distinguishing fake images tweets from real, while the performance of user based features was very poor. Our results, showed that, automated techniques can be used in identifying real images from fake images posted on Twitter.
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paper_title: TweetCred: Real-Time Credibility Assessment of Content on Twitter

paper_content:

During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or "tweets"). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting. In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in TweetCred, a real-time system that assigns a credibility score to tweets in a user's timeline. TweetCred, available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us to evaluate TweetCred in terms of response time, effectiveness and usability. To the best of our knowledge, this is the first research work to develop a real-time system for credibility on Twitter, and to evaluate it on a user base of this size.
---
paper_title: Online public communications by police & fire services during the 2012 Hurricane Sandy

paper_content:

Social media and other online communication tools are a subject of great interest in mass emergency response. Members of the public are turning to these solutions to seek and offer emergency information. Emergency responders are working to determine what social media policies should be in terms of their "public information" functions. We report on the online communications from all the coastal fire and police departments within a 100 mile radius of Hurricane Sandy's US landfall. Across four types of online communication media, we collected data from 840 fire and police departments. Findings indicate that few departments used these online channels in their Sandy response efforts, and that communications differed between fire and police departments and across media type. However, among the highly engaged departments, there is evidence that they bend and adapt policies about what constitutes appropriate public communication in the face of emergency demands; therefore, we propose that flexibility is important in considering future emergency online communication policy. We conclude with design recommendations for making online communication media more "listenable" for both emergency managers and members of the public.
---
paper_title: Tweet4act: Using incident-specific profiles for classifying crisis-related messages

paper_content:

We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods.
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paper_title: Tweedr: Mining twitter to inform disaster response

paper_content:

In this paper, we introduce Tweedr, a Twitter-mining tool that extracts actionable information for disaster relief workers during natural disasters. The Tweedr pipeline consists of three main parts: classification, clustering and extraction. In the classification phase, we use a variety of classification methods (sLDA, SVM, and logistic regression) to identify tweets reporting damage or casualties. In the clustering phase, we use filters to merge tweets that are similar to one another; and finally, in the extraction phase, we extract tokens and phrases that report specific information about different classes of infrastructure damage, damage types, and casualties. We empirically validate our approach with tweets collected from 12 different crises in the United States since 2006.
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paper_title: Whom should I follow?: identifying relevant users during crises

paper_content:

Social media is gaining popularity as a medium of communication before, during, and after crises. In several recent disasters, it has become evident that social media sites like Twitter and Facebook are an important source of information, and in cases they have even assisted in relief efforts. We propose a novel approach to identify a subset of active users during a crisis who can be tracked for fast access to information. Using a Twitter dataset that consists of 12.9 million tweets from 5 countries that are part of the "Arab Spring" movement, we show how instant information access can be achieved by user identification along two dimensions: user's location and the user's affinity towards topics of discussion. Through evaluations, we demonstrate that users selected by our approach generate more information and the quality of the information is better than that of users identified using state-of-the-art techniques.
---
paper_title: Fast unfolding of communities in large networks

paper_content:

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.
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paper_title: K.M.: Natural language processing to the rescue?: Extracting’Situational awareness’ tweets during mass emergency

paper_content:

In times of mass emergency, vast amounts of data are generated via computer-mediated communication (CMC) that are difficult to manually cull and organize into a coherent picture. Yet valuable information is broadcast, and can provide useful insight into time- and safety-critical situations if captured and analyzed properly and rapidly. We describe an approach for automatically identifying messages communicated via Twitter that contribute to situational awareness, and explain why it is beneficial for those seeking information during mass emergencies. We collected Twitter messages from four different crisis events of varying nature and magnitude and built a classifier to automatically detect messages that may contribute to situational awareness, utilizing a combination of hand-annotated and automatically-extracted linguistic features. Our system was able to achieve over 80% accuracy on categorizing tweets that contribute to situational awareness. Additionally, we show that a classifier developed for a specific emergency event performs well on similar events. The results are promising, and have the potential to aid the general public in culling and analyzing information communicated during times of mass emergency.
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paper_title: SaferCity: A System for Detecting and Analyzing Incidents from Social Media

paper_content:

This paper presents a system to identify and characterise public safety related incidents from social media, and enrich the situational awareness that law enforcement entities have on potentially-unreported activities happening in a city. The system is based on a new spatio-temporal clustering algorithm that is able to identify and characterize relevant incidents given even a small number of social media reports. We present a web-based application exposing the features of the system, and demonstrate its usefulness in detecting, from Twitter, public safety related incidents occurred in New York City during the Occupy Wall Street protests.
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paper_title: Using Social Media to Enhance Emergency Situation Awareness

paper_content:

Social media platforms, such as Twitter, offer a rich source of real-time information about real-world events, particularly during mass emergencies. Sifting valuable information from social media provides useful insight into time-critical situations for emergency officers to understand the impact of hazards and act on emergency responses in a timely manner. This work focuses on analyzing Twitter messages generated during natural disasters, and shows how natural language processing and data mining techniques can be utilized to extract situation awareness information from Twitter. We present key relevant approaches that we have investigated including burst detection, tweet filtering and classification, online clustering, and geotagging.
---
paper_title: Emergency Situation Awareness: Twitter Case Studies

paper_content:

The Emergency Situation Awareness (ESA) system provides all-hazard situation awareness information for emergency managers using content gathered from the public Twitter API. It collects, filters and analyses Tweets from specific regions of interest in near-real-time, enabling effective alerting for unexpected incidents and monitoring of emergency events with results accessible via an interactive website.
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paper_title: AIDR: artificial intelligence for disaster response

paper_content:

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.
---
paper_title: "OMG, from here, I can see the flames!": a use case of mining location based social networks to acquire spatio-temporal data on forest fires

paper_content:

The emergence of innovative web applications, often labelled as Web 2.0, has permitted an unprecedented increase of content created by non-specialist users. In particular, Location-based Social Networks (LBSN) are designed as platforms allowing the creation, storage and retrieval of vast amounts of georeferenced and user-generated contents. LBSN can thus be seen by Geographic Information specialists as a timely and cost-effective source of spatio-temporal information for many fields of application, provided that they can set up workflows to retrieve, validate and organise such information. This paper aims to improve the understanding on how LBSN can be used as a reliable source of spatio-temporal information, by analysing the temporal, spatial and social dynamics of Twitter activity during a major forest fire event in the South of France in July 2009.
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paper_title: Estimating the Locations of Emergency Events from Twitter Streams

paper_content:

Abstract Sina Weibo, the most popular microblogging service in China, plays a significance role in sharing and exchanging information on local and global level. Actually, part of the information are correlated with some events which may be local ones such as accident, protests or natural disasters to widely ones such as events concerning well-known people or political affairs. Our research interest is to detect the location where some events are occurring by analyzing only data from weibo information. In this paper, we de- note three kind of location for each weibo: (1) content-based location; (2) posting location; (3) registration location of author of weibo. There are two main contributions in our work: (a) detect event-aware location of each weibo; (b) estimate accurate event location based on sets of event-aware locations. In order to detect event-aware location, we model there kinds of location data of each weibo to tackle with detection challenge. After we obtained a set of event-aware locations, we use a hierarchical clustering to estimate the accurate event location. The result showed that our method could improve the accuracy of event location estimation.
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paper_title: Temporal summaries of new topics

paper_content:

We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.
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paper_title: Sumblr: continuous summarization of evolving tweet streams

paper_content:

With the explosive growth of microblogging services, short-text messages (also known as tweets) are being created and shared at an unprecedented rate. Tweets in its raw form can be incredibly informative, but also overwhelming. For both end-users and data analysts it is a nightmare to plow through millions of tweets which contain enormous noises and redundancies. In this paper, we study continuous tweet summarization as a solution to address this problem. While traditional document summarization methods focus on static and small-scale data, we aim to deal with dynamic, quickly arriving, and large-scale tweet streams. We propose a novel prototype called Sumblr (SUMmarization By stream cLusteRing) for tweet streams. We first propose an online tweet stream clustering algorithm to cluster tweets and maintain distilled statistics called Tweet Cluster Vectors. Then we develop a TCV-Rank summarization technique for generating online summaries and historical summaries of arbitrary time durations. Finally, we describe a topic evolvement detection method, which consumes online and historical summaries to produce timelines automatically from tweet streams. Our experiments on large-scale real tweets demonstrate the efficiency and effectiveness of our approach.
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paper_title: TREC 2013 Temporal Summarization

paper_content:

Unexpected news events such as earthquakes or natural disasters represent a unique information access problem where traditional approaches fail. For example, immediately after an event, the corpus may be sparsely populated with relevant content. Even when, after a few hours, relevant content is available, it is often inaccurate or highly redundant. At the same time, crisis events demonstrate a scenario where users urgently need information, especially if they are directly affected by the event. The goal of this track is to develop systems for efficiently monitoring the information associated with an event over time. Specifically, we are interested in developing systems which (1) can broadcast short, relevant, and reliable sentence-length updates about a developing event and (2) can track the value of important event-related attributes (e.g. number of fatalities). The track has the following goals,
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paper_title: Overview of the TAC 2008 Update Summarization Task

paper_content:

The summarization track at the Text Analysis Conference (TAC) is a direct continuation of the Document Understanding Conference (DUC) series of workshops, focused on providing common data and evaluation framework for research in automatic summarization. In the TAC 2008 summarization track, the main task was to produce two 100-word summaries from two related sets of 10 documents, where the second summary was an update summary. While all of the 71 submitted runs were automatically scored with the ROUGE and BE metrics, NIST assessors manually evaluated only 57 of the submitted runs for readability, content, and overall responsiveness.
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paper_title: Practical extraction of disaster-relevant information from social media

paper_content:

During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.
---
paper_title: Aid is Out There: Looking for Help from Tweets during a Large Scale Disaster

paper_content:

The 2011 Great East Japan Earthquake caused a wide range of problems, and as countermeasures, many aid activities were carried out. Many of these problems and aid activities were reported via Twitter. However, most problem reports and corresponding aid messages were not successfully exchanged between victims and local governments or humanitarian organizations, overwhelmed by the vast amount of information. As a result, victims could not receive necessary aid and humanitarian organizations wasted resources on redundant efforts. In this paper, we propose a method for discovering matches between problem reports and aid messages. Our system contributes to problem-solving in a large scale disaster situation by facilitating communication between victims and humanitarian organizations.
---
paper_title: Event threading within news topics

paper_content:

With the overwhelming volume of online news available today, there is an increasing need for automatic techniques to analyze and present news to the user in a meaningful and efficient manner. Previous research focused only on organizing news stories by their topics into a flat hierarchy. We believe viewing a news topic as a flat collection of stories is too restrictive and inefficient for a user to understand the topic quickly. In this work, we attempt to capture the rich structure of events and their dependencies in a news topic through our event models. We call the process of recognizing events and their dependencies event threading. We believe our perspective of modeling the structure of a topic is more effective in capturing its semantics than a flat list of on-topic stories. We formally define the novel problem, suggest evaluation metrics and present a few techniques for solving the problem. Besides the standard word based features, our approaches take into account novel features such as temporal locality of stories for event recognition and time-ordering for capturing dependencies. Our experiments on a manually labeled data sets show that our models effectively identify the events and capture dependencies among them.
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paper_title: Document update summarization using incremental hierarchical clustering

paper_content:

Document summarization has become a hot topic in recent years. However, most of existing summarization methods work on a batch of documents and do not consider that documents may arrive in a sequence and the corresponding summaries need to be updated in real time. In this paper, we propose a new summarization method based on an incremental hierarchical clustering framework to update summaries as soon as a new document arrives. Extensive experimental results demonstrate the effectiveness and efficiency of our proposed method.
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paper_title: Incident threading for news passages

paper_content:

With an overwhelming volume of news reports currently available, there is an increasing need for automatic techniques to analyze and present news to a general reader in a meaningful and efficient manner. We explore incident threading as a possible solution to this problem. All text that describes the occurrence of a real-world happening is merged into a news incident, and incidents are organized in a network with dependencies of predefined types. Earlier attempts at this problem have assumed that a news story covers a single topic. We move beyond that limitation to introduce passage threading, which processes news at the passage level. First we develop a new testbed for this research and extend the evaluation methods to address new granularity issues. Then a three-stage algorithm is described that identifies on-subject passages, groups them into incidents, and establishes links between related incidents. Finally, we observe significant improvement over earlier work when we optimize the harmonic mean of the appropriate evaluation measures. The resulting performance exceeds the level that a calibration study shows is necessary to support a reading comprehension task.
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paper_title: Recognizing Named Entities in Tweets

paper_content:

The challenges of Named Entities Recognition (NER) for tweets lie in the insufficient information in a tweet and the unavailability of training data. We propose to combine a K-Nearest Neighbors (KNN) classifier with a linear Conditional Random Fields (CRF) model under a semi-supervised learning framework to tackle these challenges. The KNN based classifier conducts pre-labeling to collect global coarse evidence across tweets while the CRF model conducts sequential labeling to capture fine-grained information encoded in a tweet. The semi-supervised learning plus the gazetteers alleviate the lack of training data. Extensive experiments show the advantages of our method over the baselines as well as the effectiveness of KNN and semi-supervised learning.
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paper_title: Named Entity Recognition in Tweets: An Experimental Study

paper_content:

People tweet more than 100 Million times daily, yielding a noisy, informal, but sometimes informative corpus of 140-character messages that mirrors the zeitgeist in an unprecedented manner. The performance of standard NLP tools is severely degraded on tweets. This paper addresses this issue by re-building the NLP pipeline beginning with part-of-speech tagging, through chunking, to named-entity recognition. Our novel T-ner system doubles F1 score compared with the Stanford NER system. T-ner leverages the redundancy inherent in tweets to achieve this performance, using LabeledLDA to exploit Freebase dictionaries as a source of distant supervision. LabeledLDA outperforms co-training, increasing F1 by 25% over ten common entity types. ::: ::: Our NLP tools are available at: http://github.com/aritter/twitter_nlp
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paper_title: Extracting information nuggets from disaster- Related messages in social media

paper_content:

Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems.
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paper_title: Updating Users About Time Critical Events ⋆

paper_content:

During unexpected events such as natural disasters, individuals rely on the information generated by news outlets to form their understanding of these events. This information, while often voluminous, is frequently degraded by the inclusion of unimportant, duplicate, or wrong information. It is important to be able to present users with only the novel, important information about these events as they develop. We present the problem of updating users about time critical news events, and focus on the task of deciding which information to select for updating users as an event develops. We propose a solution to this problem which incorporates techniques from information retrieval and multi-document summarization and evaluate this approach on a set of historic events using a large stream of news documents. We also introduce an evaluation method which is significantly less expensive than traditional approaches to temporal summarization.
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paper_title: Practical extraction of disaster-relevant information from social media

paper_content:

During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general.
---
paper_title: Resolving Surface Forms to Wikipedia Topics

paper_content:

Ambiguity of entity mentions and concept references is a challenge to mining text beyond surface-level keywords. We describe an effective method of disambiguating surface forms and resolving them to Wikipedia entities and concepts. Our method employs an extensive set of features mined from Wikipedia and other large data sources, and combines the features using a machine learning approach with automatically generated training data. Based on a manually labeled evaluation set containing over 1000 news articles, our resolution model has 85% precision and 87.8% recall. The performance is significantly better than three baselines based on traditional context similarities or sense commonness measurements. Our method can be applied to other languages and scales well to new entities and concepts.
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paper_title: EDIT: A Methodology for the Treatment of Non-authoritative Data in the Reconstruction of Disaster Scenarios

paper_content:

EDIT - Extracting Disaster Information from Text – is a methodology for the treatment of textual information extracted from the web, originated by non-expert users and referring to the occurrence of a disaster event. The project was born from a collaboration between CIMA Foundation (Italy) and the Nottingham Geospatial Institute (NGI) (United Kingdom). The methodology addresses the task of integrating unstructured knowledge into operative tools. It focuses on: the semantic analysis of online news and tweets; the automatic extraction of relevant data to enhance knowledge about disasters; the evaluation of reliability of data; the archive into an event-oriented database; the visualization of scenario maps. The study represents the starting point of a new way of approaching disaster scenarios and it is proposed that the approach is taken forward for future large-scale implementation.
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paper_title: Leveraging the Semantics of Tweets for Adaptive Faceted Search on Twitter

paper_content:

In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial effort. Users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages (tweets) and present different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a large-scale evaluation of faceted search strategies, show significant improvements over keyword search and reveal significant benefits of those strategies that (i) further enrich the semantics of tweets by exploiting links posted in tweets, and that (ii) support users in selecting facet value pairs by adapting the faceted search interface to the specific needs and preferences of a user.
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paper_title: Semantics + filtering + search = twitcident. exploring information in social web streams

paper_content:

Automatically filtering relevant information about a real-world incident from Social Web streams and making the information accessible and findable in the given context of the incident are non-trivial scientific challenges. In this paper, we engineer and evaluate solutions that analyze the semantics of Social Web data streams to solve these challenges. We introduce Twitcident, a framework and Web-based system for filtering, searching and analyzing information about real-world incidents or crises. Given an incident, our framework automatically starts tracking and filtering information that is relevant for the incident from Social Web streams and Twitter particularly. It enriches the semantics of streamed messages to profile incidents and to continuously improve and adapt the information filtering to the current temporal context. Faceted search and analytical tools allow people and emergency services to retrieve particular information fragments and overview and analyze the current situation as reported on the Social Web. We put our Twitcident system into practice by connecting it to emergency broadcasting services in the Netherlands to allow for the retrieval of relevant information from Twitter streams for any incident that is reported by those services. We conduct large-scale experiments in which we evaluate (i) strategies for filtering relevant information for a given incident and (ii) search strategies for finding particular information pieces. Our results prove that the semantic enrichment offered by our framework leads to major and significant improvements of both the filtering and the search performance. A demonstration is available via: http://wis.ewi.tudelft.nl/twitcident/
---
paper_title: Making Sense of Social Media Streams through Semantics: a Survey

paper_content:

Using semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature. This paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches to mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.
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paper_title: Using Bayesian Decision for Ontology Mapping

paper_content:

Ontology mapping is the key point to reach interoperability over ontologies. In semantic web environment, ontologies are usually distributed and heterogeneous and thus it is necessary to find the mapping between them before processing across them. Many efforts have been conducted to automate the discovery of ontology mapping. However, some problems are still evident. In this paper, ontology mapping is formalized as a problem of decision making. In this way, discovery of optimal mapping is cast as finding the decision with minimal risk. An approach called Risk Minimization based Ontology Mapping (RiMOM) is proposed, which automates the process of discoveries on 1:1, n:1, 1:null and null:1 mappings. Based on the techniques of normalization and NLP, the problem of instance heterogeneity in ontology mapping is resolved to a certain extent. To deal with the problem of name conflict in mapping process, we use thesaurus and statistical technique. Experimental results indicate that the proposed method can significantly outperform the baseline methods, and also obtains improvement over the existing methods.
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paper_title: Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data

paper_content:

This paper introduces MOAT, a lightweight Semantic Web framework that provides a collaborative way to let Web 2.0 content producers give meanings to their tags in a machine- readable way. To achieve this goal, this approach relies on Linked Data principles, using URIs from existing resources to define these meanings. That way, users can create inter- linked RDF data and let their content enter the Semantic Web, while solving some limits of free-tagging at the same time.
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paper_title: Semantic integration: a survey of ontology-based approaches

paper_content:

Semantic integration is an active area of research in several disciplines, such as databases, information-integration, and ontologies. This paper provides a brief survey of the approaches to semantic integration developed by researchers in the ontology community. We focus on the approaches that differentiate the ontology research from other related areas. The goal of the paper is to provide a reader who may not be very familiar with ontology research with introduction to major themes in this research and with pointers to different research projects. We discuss techniques for finding correspondences between ontologies, declarative ways of representing these correspondences, and use of these correspondences in various semantic-integration tasks
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paper_title: Amplifying the voice of youth in Africa via text analytics

paper_content:

U-report is an open-source SMS platform operated by UNICEF Uganda, designed to give community members a voice on issues that impact them. Data received by the system are either SMS responses to a poll conducted by UNICEF, or unsolicited reports of a problem occurring within the community. There are currently 200,000 U-report participants, and they send up to 10,000 unsolicited text messages a week. The objective of the program in Uganda is to understand the data in real-time, and have issues addressed by the appropriate department in UNICEF in a timely manner. Given the high volume and velocity of the data streams, manual inspection of all messages is no longer sustainable. This paper describes an automated message-understanding and routing system deployed by IBM at UNICEF. We employ recent advances in data mining to get the most out of labeled training data, while incorporating domain knowledge from experts. We discuss the trade-offs, design choices and challenges in applying such techniques in a real-world deployment.
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paper_title: Credibility ranking of tweets during high impact events

paper_content:

Twitter has evolved from being a conversation or opinion sharing medium among friends into a platform to share and disseminate information about current events. Events in the real world create a corresponding spur of posts (tweets) on Twitter. Not all content posted on Twitter is trustworthy or useful in providing information about the event. In this paper, we analyzed the credibility of information in tweets corresponding to fourteen high impact news events of 2011 around the globe. From the data we analyzed, on average 30% of total tweets posted about an event contained situational information about the event while 14% was spam. Only 17% of the total tweets posted about the event contained situational awareness information that was credible. Using regression analysis, we identified the important content and sourced based features, which can predict the credibility of information in a tweet. Prominent content based features were number of unique characters, swear words, pronouns, and emoticons in a tweet, and user based features like the number of followers and length of username. We adopted a supervised machine learning and relevance feedback approach using the above features, to rank tweets according to their credibility score. The performance of our ranking algorithm significantly enhanced when we applied re-ranking strategy. Results show that extraction of credible information from Twitter can be automated with high confidence.
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paper_title: TweetCred: Real-Time Credibility Assessment of Content on Twitter

paper_content:

During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or "tweets"). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting. In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in TweetCred, a real-time system that assigns a credibility score to tweets in a user's timeline. TweetCred, available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us to evaluate TweetCred in terms of response time, effectiveness and usability. To the best of our knowledge, this is the first research work to develop a real-time system for credibility on Twitter, and to evaluate it on a user base of this size.
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paper_title: Information verification during natural disasters

paper_content:

Large amounts of unverified and at times contradictory information often appear on social media following natural disasters. Timely verification of this information can be crucial to saving lives and for coordinating relief efforts. Our goal is to enable this verification by developing an online platform that involves ordinary citizens in the evidence gathering and evaluation process. The output of this platform will provide reliable information to humanitarian organizations, journalists, and decision makers involved in relief efforts.
---
paper_title: Semantics + filtering + search = twitcident. exploring information in social web streams

paper_content:

Automatically filtering relevant information about a real-world incident from Social Web streams and making the information accessible and findable in the given context of the incident are non-trivial scientific challenges. In this paper, we engineer and evaluate solutions that analyze the semantics of Social Web data streams to solve these challenges. We introduce Twitcident, a framework and Web-based system for filtering, searching and analyzing information about real-world incidents or crises. Given an incident, our framework automatically starts tracking and filtering information that is relevant for the incident from Social Web streams and Twitter particularly. It enriches the semantics of streamed messages to profile incidents and to continuously improve and adapt the information filtering to the current temporal context. Faceted search and analytical tools allow people and emergency services to retrieve particular information fragments and overview and analyze the current situation as reported on the Social Web. We put our Twitcident system into practice by connecting it to emergency broadcasting services in the Netherlands to allow for the retrieval of relevant information from Twitter streams for any incident that is reported by those services. We conduct large-scale experiments in which we evaluate (i) strategies for filtering relevant information for a given incident and (ii) search strategies for finding particular information pieces. Our results prove that the semantic enrichment offered by our framework leads to major and significant improvements of both the filtering and the search performance. A demonstration is available via: http://wis.ewi.tudelft.nl/twitcident/
---
paper_title: A Work-In-Process Literature Review: Incorporating Social Media in Risk and Crisis Communication

paper_content:

This work-in-process literature review gives an overview of recent insight in the incorporation of social media in risk and crisis communication. By marrying literature and examples of social media use with best practices in risk and crisis communication, this study demonstrates how communicators can embrace social media tools to better manage a risk or crisis. Best practices in risk and crisis communication are summarized, examples of social media tools used to manage risks and crises are expounded, and recommendations for practitioners are provided to incorporate social media tools in risk and crisis communication.
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paper_title: CrowdMonitor: Monitoring Physical and Digital Activities of Citizens During Emergencies

paper_content:

In recent times, emergencies such as the 2013 flood in mid Europe have clearly shown that besides the professional emergency services and authorities, citizens get a more and more active role in crisis response work. They organize themselves and coordinate private relief activities. Those activities can be found in (physical) groups of affected local citizens, but also within (digital) social media groups. To detect and use this civil potential by professional emergency services, approaches are needed that support the instructing of citizens and coordinating of their actions to avoid needless duplications or conflicts. In this paper we present a concept, based on a mobile crowd sensing approach, which was designed as well as implemented as the system prototype CrowdMonitor and facilitates the monitoring of physical and digital activities of and the assignment of specific tasks to citizens.
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paper_title: Aid is Out There: Looking for Help from Tweets during a Large Scale Disaster

paper_content:

The 2011 Great East Japan Earthquake caused a wide range of problems, and as countermeasures, many aid activities were carried out. Many of these problems and aid activities were reported via Twitter. However, most problem reports and corresponding aid messages were not successfully exchanged between victims and local governments or humanitarian organizations, overwhelmed by the vast amount of information. As a result, victims could not receive necessary aid and humanitarian organizations wasted resources on redundant efforts. In this paper, we propose a method for discovering matches between problem reports and aid messages. Our system contributes to problem-solving in a large scale disaster situation by facilitating communication between victims and humanitarian organizations.
---
paper_title: Understanding the utility of geospatial information in social media

paper_content:

Crisis situations generate tens of millions of social media reports, many of which contain references to geographic features and locations. Contemporary systems are now capable of mining and visualizing these location references in social media reports, but we have yet to develop a deep understanding of what end-users will expect to do with this information when attempting to achieve situational awareness. To explore this problem, we have conducted a utility and usability analysis of SensePlace2, a geovisual analytics tool designed to explore geospatial information found in Tweets. Eight users completed a task analysis and survey study using SensePlace2. Our findings reveal user expectations and key paths for solving usability and utility issues to inform the design of future visual analytics systems that incorporate geographic information from social media.
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Title: Processing Social Media Messages in Mass Emergency: A Survey

Section 1: INTRODUCTION
Description 1: Introduce the unique challenges of crisis situations and the importance of processing social media content in mass emergencies.

Section 2: Background Reading
Description 2: Provide an overview of foundational readings in the sociology of disasters and the integration of social media in emergency responses.

Section 3: Scope and Organization
Description 3: Outline the main problem this survey addresses, related applications in other fields, and a brief introduction of each section of the survey.

Section 4: USERS AND INFORMATION NEEDS
Description 4: Discuss the information needs of end-users, including emergency responders, humanitarian organizations, and the public, and how social media supports these needs.

Section 5: Public Participation in Crises
Description 5: Describe how the public uses social media to contribute to crises and how their participation can be valuable.

Section 6: Differences in Information Needs
Description 6: Explain the different types of information sought by various stakeholders during a disaster and provide examples.

Section 7: SYSTEMS FOR CRISIS-RELATED SOCIAL MEDIA MONITORING
Description 7: Examine existing systems designed to monitor social media during crises, highlighting common features and their capabilities.

Section 8: DATA CHARACTERIZATION, ACQUISITION, AND PREPARATION
Description 8: Cover the characteristics of social media messages, the methods for acquiring these data, and preprocessing techniques.

Section 9: EVENT DETECTION AND TRACKING
Description 9: Detail the methods for detecting and tracking events in social media, including retrospective and online new event detection, and event tracking.

Section 10: CLUSTERING, CLASSIFICATION, EXTRACTION, AND SUMMARIZATION
Description 10: Describe the techniques for classifying, clustering, extracting information from, and summarizing social media messages related to crises.

Section 11: SEMANTIC TECHNOLOGIES IN DISASTER RESPONSE
Description 11: Discuss the application of semantic technologies in processing social media data for disaster response, including semantic enrichment and ontologies.

Section 12: SUMMARY AND FUTURE RESEARCH DIRECTIONS
Description 12: Conclude the survey by summarizing key findings and proposing future research directions to enhance the processing of social media messages in mass emergencies.