Social media analytics: a survey of techniques, tools and platforms
---
paper_title: Computational Social Science

paper_content:

The social sciences investigate human and social dynamics and organization at all levels of analysis (consilience), including cognition, decision making, behavior, groups, organizations, societies, and the world system. Computational social science is the integrated, interdisciplinary pursuit of social inquiry with emphasis on information processing and through the medium of advanced computation. Themain computational social science areas are automated information extraction systems, social network analysis, social geographic information systems (GIS), complexity modeling, and social simulation models. Just like Galileo exploited the telescope as the key instrument for observing and gaining a deeper and empirically truthful understanding of the physical universe, computational social scientists are learning to exploit the advanced and increasingly powerful instruments of computation to see beyond the visible spectrum of more traditional disciplinary analyses.
---
paper_title: Computational Social Science

paper_content:

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.
---
paper_title: Users of the world, unite! The challenges and opportunities of Social Media

paper_content:

The concept of Social Media is top of the agenda for many business executives today. Decision makers, as well as consultants, try to identify ways in which firms can make profitable use of applications such as Wikipedia, YouTube, Facebook, Second Life, and Twitter. Yet despite this interest, there seems to be very limited understanding of what the term ''Social Media'' exactly means; this article intends to provide some clarification. We begin by describing the concept of Social Media, and discuss how it differs from related concepts such as Web 2.0 and User Generated Content. Based on this definition, we then provide a classification of Social Media which groups applications currently subsumed under the generalized term into more specific categories by characteristic: collaborative projects, blogs, content communities, social networking sites, virtual game worlds, and virtual social worlds. Finally, we present 10 pieces of advice for companies which decide to utilize Social Media.
---
paper_title: Twitter mood predicts the stock market

paper_content:

Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%. Index Terms—stock market prediction — twitter — mood analysis.
---
paper_title: Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis

paper_content:

Media reporting shapes public opinion which can in turn influence events, particularly in political elections, in which candidates both respond to and shape public perception of their campaigns. We use computational linguistics to automatically predict the impact of news on public perception of political candidates. Our system uses daily newspaper articles to predict shifts in public opinion as reflected in prediction markets. We discuss various types of features designed for this problem. The news system improves market prediction over baseline market systems.
---
paper_title: If you love something, let it go mobile: Mobile marketing and mobile social media 4x4

paper_content:

Social media applications—including collaborative projects, micro-blogs/blogs, content communities, social networking sites, and virtual worlds—have become part of the standard communication repertoire for many companies. Today, with the creation of increasingly powerful mobile devices, numerous social media applications have gone mobile and new entrants are constantly appearing. The purpose of this article is to take account of this evolution, and provide an introduction to the general topic of mobile marketing and mobile social media. Herein, we define what mobile social media is, what it is not, and how it differs from other types of mobile marketing applications. Further, we discuss how firms can make use of mobile social media for marketing research, communication, sales promotions/discounts, and relationship development/loyalty programs. We present four pieces of advice for mobile social media usage, which we refer to as the ‘Four I's’ of mobile social media. Finally, we conclude by providing some thoughts on the future evolution of this new and exciting type of application.
---
paper_title: Machine Learning: A Probabilistic Perspective

paper_content:

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
---
paper_title: Thumbs Up Or Thumbs Down? Semantic Orientation Applied To Unsupervised Classification Of Reviews

paper_content:

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g.,"subtle nuances") and a negative semantic orientation when it has bad associations (e.g.,"very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word"excellent"minus the mutual information between the given phrase and the word"poor". A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.
---
paper_title: Data Stream Management Systems for Computational Finance

paper_content:

Because financial applications rely on a continual stream of time-sensitive data, any data management system must be able to process complex queries on the fly. Although many organizations turn to custom solutions, data stream management systems can offer the same low-latency processing with the flexibility to handle a range of applications.
---
paper_title: SECRET: A Model for Analysis of the Execution Semantics of Stream Processing Systems

paper_content:

There are many academic and commercial stream processing engines (SPEs) today, each of them with its own execution semantics. This variation may lead to seemingly inexplicable differences in query results. In this paper, we present SECRET, a model of the behavior of SPEs. SECRET is a descriptive model that allows users to analyze the behavior of systems and understand the results of window-based queries for a broad range of heterogeneous SPEs. The model is the result of extensive analysis and experimentation with several commercial and academic engines. In the paper, we describe the types of heterogeneity found in existing engines, and show with experiments on real systems that our model can explain the key differences in windowing behavior.
---
paper_title: Data stream management and mining

paper_content:

This paper provides an introduction to the field of data stream management and mining. The increase of data production in operational information systems prevents from monitoring these systems with the old paradigm of storing data before analyzing it. New approaches have been developed recently to process data 'on the fly' considering they are produced in the form of structured data streams. These approaches cover both querying and mining data.
---
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.
---
paper_title: Information retrieval on the web

paper_content:

In this paper we review studies of the growth of the Internet and technologies that are useful for information search and retrieval on the Web. We present data on the Internet from several different sources, e.g., current as well as projected number of users, hosts, and Web sites. Although numerical figures vary, overall trends cited by the sources are consistent and point to exponential growth in the past and in the coming decade. Hence it is not surprising that about 85% of Internet users surveyed claim using search engines and search services to find specific information. The same surveys show, however, that users are not satisfied with the performance of the current generation of search engines; the slow retrieval speed, communication delays, and poor quality of retrieved results (e.g., noise and broken links) are commonly cited problems. We discuss the development of new techniques targeted to resolve some of the problems associated with Web-based information retrieval and speculate on future trends.
---
paper_title: Comparative Analysis of Data Mining Tools and Techniques for Evaluating Performance of Database System

paper_content:

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Nowadays, large amount of data and information are available, Data can now be stored in many different kinds of databases and information repositories, being available on the Internet. There is a need for powerful techniques for better interpretation of these data that exceeds the human's ability for comprehension and making decision in a better way. There are data mining, web mining and knowledge discovery tools and software packages such as WEKA Tool and RapidMiner tool. The work deals with analysis of WEKA, RapidMiner and NetTools spider tools KNIME and Orange. There are various tools available for data mining and web mining. Therefore awareness is required about the quantitative investigation of these tools. This paper focuses on various functional, practical, cognitive as well as analysis aspects that users may be looking for in the tools. Complete study addresses the usefulness and importance of these tools including various aspects. Analysis presents various benefits of these data mining tools along with desired aspects and the features of current tools.
---
paper_title: From NLP (natural language processing) to MLP (machine language processing)

paper_content:

Natural Language Processing (NLP) in combination with Machine Learning techniques plays an important role in the field of automatic text analysis. Motivated by the successful use of NLP in solving text classification problems in the area of e-Participation and inspired by our prior work in the field of polymorphic shellcode detection we gave classical NLP-processes a trial in the special case of malicious code analysis. Any malicious program is based on some kind of machine language, ranging from manually crafted assembler code that exploits a buffer overflow to high level languages such as Javascript used in web-based attacks. We argue that well known NLP analysis processes can be modified and applied to the malware analysis domain. Similar to the NLP process we call this process Machine Language Processing (MLP). In this paper, we use our e-Participation analysis architecture, extract the various NLP techniques and adopt them for the malware analysis process. As proof-of-concept we apply the adopted framework to malicious code examples from Metasploit.
---

Title: Social Media Analytics: A Survey of Techniques, Tools and Platforms

Section 1: Introduction
Description 1: Provide an overview of social media, its definition, significance for computational social science, and the challenges associated with the tools for researchers.

Section 2: Terminology
Description 2: Define key techniques related to analyzing unstructured textual data including Natural Language Processing, News Analytics, Opinion Mining, Scraping, Sentiment Analysis, and Text Analytics.

Section 3: Research Challenges
Description 3: Discuss the academic research challenges in social media scraping and analytics, including scraping difficulties, data cleansing, combining holistic data sources, data protection, data analytics, the need for non-programming interfaces, and data visualization.

Section 4: Social Media Research and Applications
Description 4: Explore the innovative research and applications of social media data in various fields such as business, bioscience, and social science.

Section 5: Social Media Overview
Description 5: Categorize social media tools and platforms into types of data, programmatic access, transformation tools, analysis tools, and comprehensive social media platforms.

Section 6: Social Media Methodology and Critique
Description 6: Outline the critical methodologies and a critique regarding access to comprehensive data sets and tools for deep data analysis without extensive programming requirements.

Section 7: Methodology
Description 7: Delve into the requirements for social media research, focusing on data access, analytics capabilities, and the facilities needed to support such research.

Section 8: Social Media Data
Description 8: Categorize and discuss the types of data and text data formats commonly used in social media, including raw and cleaned data, and historical and real-time data.

Section 9: Text Cleaning, Tagging, and Storing
Description 9: Review techniques and strategies for cleaning, tagging, and storing unstructured social media data to ensure high-quality analytics.

Section 10: Social Media Analytics Techniques
Description 10: Provide an overview of computational science techniques deployed in social media analytics, including computational statistics, machine learning, complexity science, data mining, and simulation modeling.

Section 11: Sentiment Analysis
Description 11: Explore the methods and subtasks of sentiment analysis, including sentiment context, levels, subjectivity, orientation/polarity, strength, and estimation techniques.

Section 12: Social Media Analytics Tools
Description 12: Review various individual tools and toolkits for scientific programming, business applications, social media monitoring, text analysis, and data visualization.

Section 13: Social Media Analytics Platforms
Description 13: Examine comprehensive social media platforms that provide archives, real-time data feeds, data mining, and analysis tools for deeper insights.

Section 14: Experimental Computational Environment for Social Media
Description 14: Discuss the system architecture and components necessary for a comprehensive experimental computational environment for social media research.

Section 15: Conclusions
Description 15: Summarize the findings, address the concerns regarding data access restrictions, and highlight the need for public-domain computational environments and data facilities for research.