Survey of Temporal Information Retrieval and Related Applications
---
paper_title: Time is of the essence: improving recency ranking using Twitter data

paper_content:

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.
---
paper_title: Temporal Web Image Retrieval

paper_content:

Temporal Web Image Retrieval can be defined as the process that retrieves sets of Web images with their temporal dimension from explicit or implicit temporal text queries. Supposing that (a) the temporal dimension is included in image indexing and (b) the query is explicitly expressed with a time tag (e.g. "Fukushima 2011"), the retrieval task can be straightforward as image retrieval has been studied for several years with success. However, text queries are usually implicit in time (e.g. "Second World War") and automatically capturing the time dimension included in Web images is a challenge that has not been studied so far to the best of our knowledge. In this paper, we will discuss different research issues about Temporal Web Image Retrieval and the current progresses of our research in temporal ephemeral clustering and temporal image filtering.
---
paper_title: Effectiveness of Temporal Snippets

paper_content:

We introduce the notion of time-centered snippets, called TSnippet, as document surrogates for document retrieval and exploration. We propose an alternative document snippet based on temporal information that can be useful for supporting exploratory search. The idea of using sentences that contain the most frequent chronons (units of time) can be used for constructing document surrogates. We conducted a series of experiments to evaluate this new approach using a crowdsourcing approach. The evaluation against two Web search engines shows that our technique produces good snippets and users like to see time-sensitive information in search results.
---
paper_title: Creating a billion-scale searchable web archive

paper_content:

Web information is ephemeral. Several organizations around the world are struggling to archive information from the web before it vanishes. However, users demand efficient and effective search mechanisms to access the already vast collections of historical information held by web archives. The Portuguese Web Archive is the largest full-text searchable web archive publicly available. It supports search over 1.2 billion files archived from the web since 1996. This study contributes with an overview of the lessons learned while developing the Portuguese Web Archive, focusing on web data acquisition, ranking search results and user interface design. The developed software is freely available as an open source project. We believe that sharing our experience obtained while developing and operating a running service will enable other organizations to start or improve their web archives.
---
paper_title: Towards recency ranking in web search

paper_content:

In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detects and responds to recency sensitive queries. The system detects recency sensitive queries using a high precision classifier. The system responds to recency sensitive queries by using a machine learned ranking model trained for such queries. We use multiple recency features to provide temporal evidence which effectively represents document recency. Furthermore, we propose several training methodologies important for training recency sensitive rankers. Finally, we develop new evaluation metrics for recency sensitive queries. Our experiments demonstrate the efficacy of the proposed approaches.
---
paper_title: Learning to rank search results for time-sensitive queries

paper_content:

Retrieval effectiveness of temporal queries can be improved by taking into account the time dimension. Existing temporal ranking models follow one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, and 2) a probabilistic model generating a query from the textual and temporal part of document independently. In this paper, we propose a novel time-aware ranking model based on learning-to-rank techniques. We employ two classes of features for learning a ranking model, entity-based and temporal features, which are derived from annotation data. Entity-based features are aimed at capturing the semantic similarity between a query and a document, whereas temporal features measure the temporal similarity. Through extensive experiments we show that our ranking model significantly improves the retrieval effectiveness over existing time-aware ranking models.
---
paper_title: Learning to rank for freshness and relevance

paper_content:

Freshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search engine appear stale. While freshness and relevance can be closely related for some topics (e.g., news queries), they are more independent in others (e.g., time insensitive queries). Therefore, optimizing one criterion does not necessarily improve the other, and can even do harm in some cases. We propose a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating different temporal characteristics of queries, and the features that can be used for capturing these properties. We then introduce our supervised framework that leverages the temporal profile of queries (inferred from pseudo-feedback documents) along with the other ranking features to improve both freshness and relevance of search results. Our experiments on a large archival web corpus demonstrate the efficacy of our techniques.
---
paper_title: Supporting analysis of future-related information in news archives and the web

paper_content:

A lot of future-related information is available in news articles or Web pages. This information can however differ to large extent and may fluctuate over time. It is therefore difficult for users to manually compare and aggregate it, and to re-construct the most probable course of future events. In this paper we approach a problem of automatically generating summaries of future events related to queries using data obtained from news archive collections or from the Web. We propose two methods, explicit and implicit future-related information detection. The former is based on analyzing the context of future temporal expressions in documents, while the latter relies on detecting periodical patterns in historical document collections. We present a graph-based visualization of future-related information and demonstrate its usefulness through several examples.
---
paper_title: Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets

paper_content:

With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would be interesting to have two separate clusters {1974,2006} corresponding to each of the two temporal instances. However, clustering of search results by time is a non-trivial task that involves determining the most relevant dates associated to a query. In this paper, we propose a first approach to flat temporal clustering of search results. We rely on a second order co-occurrence similarity measure approach which first identifies top relevant dates. Documents are grouped at the year level, forming the temporal instances of the query. Experimental tests were performed using real-world text queries. We used several measures for evaluating the performance of the system and compared our approach with Carrot Web-snippet clustering engine. Both experiments were complemented with a user survey.
---
paper_title: Leveraging temporal dynamics of document content in relevance ranking

paper_content:

Many web documents are dynamic, with content changing in varying amounts at varying frequencies. However, current document search algorithms have a static view of the document content, with only a single version of the document in the index at any point in time. In this paper, we present the first published analysis of using the temporal dynamics of document content to improve relevance ranking. We show that there is a strong relationship between the amount and frequency of content change and relevance. We develop a novel probabilistic document ranking algorithm that allows differential weighting of terms based on their temporal characteristics. By leveraging such content dynamics we show significant performance improvements for navigational queries.
---
paper_title: Temporal profiles of queries

paper_content:

Documents with timestamps, such as email and news, can be placed along a timeline. The timeline for a set of documents returned in response to a query gives an indication of how documents relevant to that query are distributed in time. Examining the timeline of a query result set allows us to characterize both how temporally dependent the topic is, as well as how relevant the results are likely to be. We outline characteristic patterns in query result set timelines, and show experimentally that we can automatically classify documents into these classes. We also show that properties of the query result set timeline can help predict the mean average precision of a query. These results show that meta-features associated with a query can be combined with text retrieval techniques to improve our understanding and treatment of text search on documents with timestamps.
---
paper_title: Clustering of search results using temporal attributes

paper_content:

Clustering of search results is an important feature in many of today's information retrieval applications. The notion of hit list clustering appears in Web search engines and enterprise search engines as a mechanism that allows users to further explore the coverage of a query. However, there has been little work on exposing temporal attributes for constructing and presentation of clusters. These attributes appear in documents as part of the textual content, e.g., as a date and time token or as a temporal reference in a sentence. In this paper, we outline a model and describe a prototype that shows the main ideas.
---
paper_title: Quantitative Analysis of Culture Using Millions of Digitized Books

paper_content:

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.
---
paper_title: Time-based language models

paper_content:

We explore the relationship between time and relevance using TREC ad-hoc queries. A type of query is identified that favors very recent documents. We propose a time-based language model approach to retrieval for these queries. We show how time can be incorporated into both query-likelihood models and relevance models. These models were used for experiments comparing time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.
---
paper_title: TimeML: Robust Specification of Event and Temporal Expressions in Text

paper_content:

In this paper we provide a description of TimeML, a rich specification language for event and temporal expressions in natural language text, developed in the context of the AQUAINT program on Question Answering Systems. Unlik em ost previous work on event annotation, TimeML capture st hree distinct phenomena in temporal markup: (1) it systematically anchors event predicates to a broad range of temporally denotating expressions; (2) it orders event expressions in text relative to one another, both intrasententially and in discourse; and (3) it allows for a delayed (underspecified) interpretation of partially determined temporal expressions. We demonstrate the expressiveness of TimeML for a broad range of syntactic and semantic contexts, including aspectual predication, modal subordination, and an initial treatment of lexical and constructional causation in text.
---
paper_title: Maintaining knowledge about temporal intervals

paper_content:

An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
---
paper_title: Annotating Events and Temporal Information in Newswire Texts

paper_content:

If one is concerned with natural language processing applications such as information extraction (IE), which typically involve extracting information about temporally situated scenarios, the ability to accurately position key events in time is of great importance. To date only minimal work has been done in the IE community concerning the extraction of temporal information from text, and the importance, together with the difficulty of the task, suggest that a concerted effort be made to analyse how temporal information is actually conveyed in real texts. To this end we have devised an annotation scheme for annotating those features and relations in texts which enable us to determine the relative order and, if possible, the absolute time, of the events reported in them. Such a scheme could be used to construct an annotated corpus which would yield the benefits normally associated with the construction of such resources: a better understanding of the phenomena of concern, and a resource for the training and evaluation of adaptive algorithms to automatically identify features and relations of interest. We also describe a framework for evaluating the annotation and compute precision and recall for different responses.
---
paper_title: Topic Detection and Tracking Pilot Study Final Report

paper_content:

Topic Detection and Tracking (TDT) is a DARPA-sponsored initiative to investigate the state of the art in finding and following new events in a stream of broadcast news stories. The TDT problem consists of three major tasks: (1) segmenting a stream of data, especially recognized speech, into distinct stories; (2) identifying those news stories that are the first to discuss a new event occurring in the news; and (3) given a small number of sample news stories about an event, finding all following stories in the stream.
---
paper_title: Text classification and named entities for new event detection

paper_content:

New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.
---
paper_title: Utilizing Temporal Information in Topic Detection and Tracking

paper_content:

The harnessing of time-related information from text for the use of information retrieval requires a leap from the surface forms of the expressions to a formalized time-axis. Often the expressions are used to form chronological sequences of events. However, we want to be able to determine the temporal similarity, i.e., the overlap of temporal references of two documents and use this similarity in Topic Detection and Tracking, for example. We present a methodology for extraction of temporal expressions and a scheme of comparing the temporal evidence of the news documents. We also examine the behavior of the temporal expressions and run experiments on English News corpus.
---
paper_title: Extracting significant time varying features from text

paper_content:

We propose a simple statistical model for the frequency of occurrence of features in a stream of text. Adoption of this model allows us to use classical significance tests to filter the stream for interesting events. We tested the model by building a system and running it on a news corpus. By a subjective evaluation, the system worked remarkably well: almost all of the groups of identified tokens corresponded to news stories and were appropriately placed in time. A preliminary objective evaluation was also used to measure the quality of the system and it showed some of the weaknesses and the power of our approach.
---
paper_title: Events Retrieval Using Enhanced Semantic Web Knowledge

paper_content:

In this article, we present an experimental end user application to query DeRiVE 2011 challenge dataset in an innovative and intuitive manner. After enriching the dataset with external sources of information, it is indexed in a way that enables users to submit queries combining keywords, location and temporal anchor, in a single search field. The goal is to ease event retrieval providing a simple user interface to query and visualize events over time.
---
paper_title: Annotating Events and Temporal Information in Newswire Texts

paper_content:

If one is concerned with natural language processing applications such as information extraction (IE), which typically involve extracting information about temporally situated scenarios, the ability to accurately position key events in time is of great importance. To date only minimal work has been done in the IE community concerning the extraction of temporal information from text, and the importance, together with the difficulty of the task, suggest that a concerted effort be made to analyse how temporal information is actually conveyed in real texts. To this end we have devised an annotation scheme for annotating those features and relations in texts which enable us to determine the relative order and, if possible, the absolute time, of the events reported in them. Such a scheme could be used to construct an annotated corpus which would yield the benefits normally associated with the construction of such resources: a better understanding of the phenomena of concern, and a resource for the training and evaluation of adaptive algorithms to automatically identify features and relations of interest. We also describe a framework for evaluating the annotation and compute precision and recall for different responses.
---
paper_title: Effectiveness of Temporal Snippets

paper_content:

We introduce the notion of time-centered snippets, called TSnippet, as document surrogates for document retrieval and exploration. We propose an alternative document snippet based on temporal information that can be useful for supporting exploratory search. The idea of using sentences that contain the most frequent chronons (units of time) can be used for constructing document surrogates. We conducted a series of experiments to evaluate this new approach using a crowdsourcing approach. The evaluation against two Web search engines shows that our technique produces good snippets and users like to see time-sensitive information in search results.
---
paper_title: From Temporal Expressions To Temporal Information: Semantic Tagging Of News Messages

paper_content:

We present a semantic tagging system for temporal expressions and discuss how the temporal information conveyed by these expressions can be extracted. The performance of the system was evaluated wrt. a small hand-annotated corpus of news messages.
---
paper_title: Extracting and aggregating temporal events from text

paper_content:

Finding reliable information about a given event from large and dynamic text collections is a topic of great interest. For instance, rescue teams and insurance companies are interested in concise facts about damages after disasters, which can be found in web blogs, newspaper articles, social networks etc. However, finding, extracting, and condensing specific facts is a highly complex undertaking: It requires identifying appropriate textual sources, recognizing relevant facts within the sources, and aggregating extracted facts into a condensed answer despite inconsistencies, uncertainty, and changes over time. In this paper, we present a three-step framework providing techniques and solutions for each of these problems. We tested the feasibility of extracting time-associated event facts using our framework in a comprehensive case study: gathering data on particular earthquakes from web data sources. Our results show that it is, under certain circumstances, possible to automatically obtain reliable and timely data on natural disasters from the web.
---
paper_title: SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations

paper_content:

Within the SemEval-2013 evaluation exercise, the TempEval-3 shared task aims to advance research on temporal information processing. It follows on from TempEval-1 and -2, with: a three-part structure covering temporal expression, event, and temporal relation extraction; a larger dataset; and new single measures to rank systems ‐ in each task and in general. In this paper, we describe the participants’ approaches, results, and the observations from the results, which may guide future research in this area.
---
paper_title: Creating a billion-scale searchable web archive

paper_content:

Web information is ephemeral. Several organizations around the world are struggling to archive information from the web before it vanishes. However, users demand efficient and effective search mechanisms to access the already vast collections of historical information held by web archives. The Portuguese Web Archive is the largest full-text searchable web archive publicly available. It supports search over 1.2 billion files archived from the web since 1996. This study contributes with an overview of the lessons learned while developing the Portuguese Web Archive, focusing on web data acquisition, ranking search results and user interface design. The developed software is freely available as an open source project. We believe that sharing our experience obtained while developing and operating a running service will enable other organizations to start or improve their web archives.
---
paper_title: DBpedia: A Nucleus for a Web of Open Data

paper_content:

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for human-andmachine-consumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.
---
paper_title: Coupling Label Propagation and Constraints for Temporal Fact Extraction

paper_content:

The Web and digitized text sources contain a wealth of information about named entities such as politicians, actors, companies, or cultural landmarks. Extracting this information has enabled the automated construction of large knowledge bases, containing hundred millions of binary relationships or attribute values about these named entities. However, in reality most knowledge is transient, i.e. changes over time, requiring a temporal dimension in fact extraction. In this paper we develop a methodology that combines label propagation with constraint reasoning for temporal fact extraction. Label propagation aggressively gathers fact candidates, and an Integer Linear Program is used to clean out false hypotheses that violate temporal constraints. Our method is able to improve on recall while keeping up with precision, which we demonstrate by experiments with biography-style Wikipedia pages and a large corpus of news articles.
---
paper_title: Robust Temporal Processing Of News

paper_content:

We introduce an annotation scheme for temporal expressions, and describe a method for resolving temporal expressions in print and broadcast news. The system, which is based on both hand-crafted and machine-learnt rules, achieves an 83.2% accuracy (F-measure) against hand-annotated data. Some initial steps towards tagging event chronologies are also described.
---
paper_title: Harvesting facts from textual web sources by constrained label propagation

paper_content:

There have been major advances on automatically constructing large knowledge bases by extracting relational facts from Web and text sources. However, the world is dynamic: periodic events like sports competitions need to be interpreted with their respective timepoints, and facts such as coaching a sports team, holding political or business positions, and even marriages do not hold forever and should be augmented by their respective timespans. This paper addresses the problem of automatically harvesting temporal facts with such extended time-awareness. We employ pattern-based gathering techniques for fact candidates and construct a weighted pattern-candidate graph. Our key contribution is a system called PRAVDA based on a new kind of label propagation algorithm with a judiciously designed loss function, which iteratively processes the graph to label good temporal facts for a given set of target relations. Our experiments with online news and Wikipedia articles demonstrate the accuracy of this method.
---
paper_title: Coupled temporal scoping of relational facts

paper_content:

Recent research has made significant advances in automatically constructing knowledge bases by extracting relational facts (e.g., Bill Clinton-presidentOf-US) from large text corpora. Temporally scoping such relational facts in the knowledge base (i.e., determining that Bill Clinton-presidentOf-US is true only during the period 1993 - 2001) is an important, but relatively unexplored problem. In this paper, we propose a joint inference framework for this task, which leverages fact-specific temporal constraints, and weak supervision in the form of a few labeled examples. Our proposed framework, CoTS (Coupled Temporal Scoping), exploits temporal containment, alignment, succession, and mutual exclusion constraints among facts from within and across relations. Our contribution is multi-fold. Firstly, while most previous research has focused on micro-reading approaches for temporal scoping, we pose it in a macro-reading fashion, as a change detection in a time series of facts' features computed from a large number of documents. Secondly, to the best of our knowledge, there is no other work that has used joint inference for temporal scoping. We show that joint inference is effective compared to doing temporal scoping of individual facts independently. We conduct our experiments on large scale open-domain publicly available time-stamped datasets, such as English Gigaword Corpus and Google Books Ngrams, demonstrating CoTS's effectiveness.
---
paper_title: Quantitative Analysis of Culture Using Millions of Digitized Books

paper_content:

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.
---
paper_title: Timely YAGO: harvesting, querying, and visualizing temporal knowledge from Wikipedia

paper_content:

Recent progress in information extraction has shown how to automatically build large ontologies from high-quality sources like Wikipedia. But knowledge evolves over time; facts have associated validity intervals. Therefore, ontologies should include time as a first-class dimension. In this paper, we introduce Timely YAGO, which extends our previously built knowledge base YAGO with temporal aspects. This prototype system extracts temporal facts from Wikipedia infoboxes, categories, and lists in articles, and integrates these into the Timely YAGO knowledge base. We also support querying temporal facts, by temporal predicates in a SPARQL-style language. Visualization of query results is provided in order to better understand of the dynamic nature of knowledge.
---
paper_title: TimeML: Robust Specification of Event and Temporal Expressions in Text

paper_content:

In this paper we provide a description of TimeML, a rich specification language for event and temporal expressions in natural language text, developed in the context of the AQUAINT program on Question Answering Systems. Unlik em ost previous work on event annotation, TimeML capture st hree distinct phenomena in temporal markup: (1) it systematically anchors event predicates to a broad range of temporally denotating expressions; (2) it orders event expressions in text relative to one another, both intrasententially and in discourse; and (3) it allows for a delayed (underspecified) interpretation of partially determined temporal expressions. We demonstrate the expressiveness of TimeML for a broad range of syntactic and semantic contexts, including aspectual predication, modal subordination, and an initial treatment of lexical and constructional causation in text.
---
paper_title: Using neighbors to date web documents

paper_content:

Time has been successfully used as a feature in web information retrieval tasks. In this context, estimating a document's inception date or last update date is a necessary task. Classic approaches have used HTTP header fields to estimate a document's last update time. The main problem with this approach is that it is applicable to a small part of web documents. In this work, we evaluate an alternative strategy based on a document's neighborhood. Using a random sample containing 10,000 URLs from the Yahoo! Directory, we study each document's links and media assets to determine its age. If we only consider isolated documents, we are able to date 52% of them. Including the document's neighborhood, we are able to estimate the date of more than 86% of the same sample. Also, we find that estimates differ significantly according to the type of neighbors used. The most reliable estimates are based on the document's media assets, while the worst estimates are based on incoming links. These results are experimentally evaluated with a real world application using different datasets.
---
paper_title: What is the Temporal Value of Web Snippets?

paper_content:

World Wide Web (WWW) is a huge information network from which retrieving and organizing quality relevant content remains an open question for mostly all implicit temporal queries, i.e., queries without any date but with an underlying temporal intent. In this research, we aim at studying the temporal nature of any given query by means of web snippets or web query logs. For that purpose, we conducted a set of experiments, which goal is to assess the percentage of web snippets or queries (in query logs) having temporal features, thus checking whether they are a valuable source of data to help on inferring the temporal intent of queries, namely implicit ones. Our results show that web snippets, as opposed to web query logs, are an important source of concentrated information, where time clues often appear. As a consequence, they can be particularly useful to identify and understand "on-the-fly" the implicit temporal nature of queries in the context of ephemeral clustering.
---
paper_title: Using Web Snippets and Web Query-logs to Measure Implicit Temporal Intents in Queries

paper_content:

Understanding the user's temporal intent by means of query formulation is a particular hard task that can become even more difficult if the user is not clear in his purpose. For example, a user who issues the query Lady Gaga may wish to find the official web site of this popular singer or other information such as informative or even rumor texts. But, he may also wish to explore biographic data, temporal information on discography release and expected tour dates. Finding this information, however, may prove to be particularly difficult, if the user does not specify the query in terms of temporal intent. Thus, having access to this data, will allow search mechanisms to improve search results especially for time-implicit queries. In this paper, we study different approaches to automatically determine the temporal nature of queries. On the one hand, we exploit web snippets, a content-related resource. On the other hand, we exploit Google and Yahoo! completion engines, which provide query-log resources. From these resources, we propose different measures to understand the temporal nature of queries. We compare these measures by analyzing their correlation. Finally, we conduct a user study to temporally label queries.
---
paper_title: Understanding temporal query dynamics

paper_content:

Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., earthquake) and others remaining relatively constant (e.g., youtube). The documents indexed by the search engine also change, with some documents always being about a particular query (e.g., the Wikipedia page on earthquakes is about the query earthquake) and others being about the query only at a particular point in time (e.g., the New York Times is only about earthquakes following a major seismic activity). The relationship between documents and queries can also change as people's intent changes (e.g., people sought different content for the query earthquake before the Haitian earthquake than they did after). In this paper, we explore how queries, their associated documents, and the query intent change over the course of 10 weeks by analyzing query log data, a daily Web crawl, and periodic human relevance judgments. We identify several interesting features by which changes to query popularity can be classified, and show that presence of these features, when accompanied by changes in result content, can be a good indicator of change in query intent.
---
paper_title: Panel on use of proprietary data

paper_content:

A panel discussion on the use of proprietary data was held at SIGIR 2012 in Portland. This report summarizes the positions put forward by the six panelists and the points that arose during the wider discussion that followed.
---
paper_title: Use of temporal expressions in web search

paper_content:

While trying to understand and characterize users' behavior online, the temporal dimension has received little attention by the research community. This exploratory study uses two collections of web search queries to investigate the use of temporal information needs. Using state-of-the-art information extraction techniques we identify temporal expressions in these queries. We find that temporal expressions are rarely used (1.5% of queries) and, when used, they are related to current and past events. Also, there are specific topics where the use of temporal expressions is more visible.
---
paper_title: Temporal profiles of queries

paper_content:

Documents with timestamps, such as email and news, can be placed along a timeline. The timeline for a set of documents returned in response to a query gives an indication of how documents relevant to that query are distributed in time. Examining the timeline of a query result set allows us to characterize both how temporally dependent the topic is, as well as how relevant the results are likely to be. We outline characteristic patterns in query result set timelines, and show experimentally that we can automatically classify documents into these classes. We also show that properties of the query result set timeline can help predict the mean average precision of a query. These results show that meta-features associated with a query can be combined with text retrieval techniques to improve our understanding and treatment of text search on documents with timestamps.
---
paper_title: What is the Temporal Value of Web Snippets?

paper_content:

World Wide Web (WWW) is a huge information network from which retrieving and organizing quality relevant content remains an open question for mostly all implicit temporal queries, i.e., queries without any date but with an underlying temporal intent. In this research, we aim at studying the temporal nature of any given query by means of web snippets or web query logs. For that purpose, we conducted a set of experiments, which goal is to assess the percentage of web snippets or queries (in query logs) having temporal features, thus checking whether they are a valuable source of data to help on inferring the temporal intent of queries, namely implicit ones. Our results show that web snippets, as opposed to web query logs, are an important source of concentrated information, where time clues often appear. As a consequence, they can be particularly useful to identify and understand "on-the-fly" the implicit temporal nature of queries in the context of ephemeral clustering.
---
paper_title: What's new on the web?: the evolution of the web from a search engine perspective

paper_content:

We seek to gain improved insight into how Web search engines shouldcope with the evolving Web, in an attempt to provide users with themost up-to-date results possible. For this purpose we collectedweekly snapshots of some 150 Web sites over the course of one year,and measured the evolution of content and link structure. Our measurements focus on aspects of potential interest to search engine designers: the evolution of link structure over time, the rate ofcreation of new pages and new distinct content on the Web, and the rate of change of the content of existing pages under search-centric measures of degree of change.Our findings indicate a rapid turnover rate of Web pages, i.e.,high rates of birth and death, coupled with an even higher rate ofturnover in the hyperlinks that connect them. For pages that persistover time we found that, perhaps surprisingly, the degree of contentshift as measured using TF.IDF cosine distance does not appear to beconsistently correlated with the frequency of contentupdating. Despite this apparent non-correlation, the rate of content shift of a given page is likely to remain consistent over time. That is, pages that change a great deal in one week will likely change by a similarly large degree in the following week. Conversely, pages that experience little change will continue to experience little change. We conclude the paper with a discussion of the potential implications ofour results for the design of effective Web search engines.
---
paper_title: Recovering a website's server components from the web infrastructure

paper_content:

Our previous research has shown that the collective behavior of search engine caches (e.g., Google, Yahoo, Live Search) and web archives (e.g., Internet Archive) results in the uncoordinated but large-scale refreshing and migrating of web resources. Interacting with these caches and archives, which we call the Web Infrastructure (WI), allows entire websites to be reconstructed in an approach we call lazy preservation. Unfortunately, the WI only captures the client-side view of a web resource. While this may be useful for recovering much of the content of a website, it is not helpful for restoring the scripts, web server configuration, databases, and other server-side components responsible for the construction of the website's resources. This paper proposes a novel technique for storing and recovering the server-side components of a website from the WI. Using erasure codes to embed the server-side components as HTML comments throughout the website, we can effectively reconstruct all the server components of a website when only a portion of the client-side resources have been extracted from the WI. We present the results of a preliminary study that baselines the lazy preservation of ten EPrints repositories and then examines the preservation of an EPrints repository that uses the erasure code technique to store the server-side EPrints software throughout the website. We found nearly 100% of the EPrints components were recoverable from the WI just two weeks after the repository came online, and it remained recoverable four months after it was "lost".
---
paper_title: The web changes everything: understanding the dynamics of web content

paper_content:

The Web is a dynamic, ever changing collection of information. This paper explores changes in Web content by analyzing a crawl of 55,000 Web pages, selected to represent different user visitation patterns. Although change over long intervals has been explored on random (and potentially unvisited) samples of Web pages, little is known about the nature of finer grained changes to pages that are actively consumed by users, such as those in our sample. We describe algorithms, analyses, and models for characterizing changes in Web content, focusing on both time (by using hourly and sub-hourly crawls) and structure (by looking at page-, DOM-, and term-level changes). Change rates are higher in our behavior-based sample than found in previous work on randomly sampled pages, with a large portion of pages changing more than hourly. Detailed content and structure analyses identify stable and dynamic content within each page. The understanding of Web change we develop in this paper has implications for tools designed to help people interact with dynamic Web content, such as search engines, advertising, and Web browsers.
---
paper_title: Memento: Time Travel for the Web

paper_content:

The Web is ephemeral. Many resources have representations that change over time, and many of those representations are lost forever. A lucky few manage to reappear as archived resources that carry their own URIs. For example, some content management systems maintain version pages that reect a frozen prior state of their changing resources. Archives recurrently crawl the web to obtain the actual representation of resources, and subsequently make those available via special-purpose archived resources. In both cases, the archival copies have URIs that are protocolwise disconnected from the URI of the resource of which they represent a prior state. Indeed, the lack of temporal capabilities in the most common Web protocol, HTTP, prevents getting to an archived resource on the basis of the URI of its original. This turns accessing archived resources into a signicant discovery challenge for both human and software agents, which typically involves following a multitude of links from the original to the archival resource, or of searching archives for the original URI. This paper proposes the protocol-based Memento solution to address this problem, and describes a proof-of-concept experiment that includes major servers of archival content, including Wikipedia and the Internet Archive. The Memento solution is based on existing HTTP capabilities applied in a novel way to add the temporal dimension. The result is a framework in which archived resources can seamlessly be reached via the URI of their original: protocol-based time travel for the Web.
---
paper_title: Creating a billion-scale searchable web archive

paper_content:

Web information is ephemeral. Several organizations around the world are struggling to archive information from the web before it vanishes. However, users demand efficient and effective search mechanisms to access the already vast collections of historical information held by web archives. The Portuguese Web Archive is the largest full-text searchable web archive publicly available. It supports search over 1.2 billion files archived from the web since 1996. This study contributes with an overview of the lessons learned while developing the Portuguese Web Archive, focusing on web data acquisition, ranking search results and user interface design. The developed software is freely available as an open source project. We believe that sharing our experience obtained while developing and operating a running service will enable other organizations to start or improve their web archives.
---
paper_title: Understanding temporal query dynamics

paper_content:

Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., earthquake) and others remaining relatively constant (e.g., youtube). The documents indexed by the search engine also change, with some documents always being about a particular query (e.g., the Wikipedia page on earthquakes is about the query earthquake) and others being about the query only at a particular point in time (e.g., the New York Times is only about earthquakes following a major seismic activity). The relationship between documents and queries can also change as people's intent changes (e.g., people sought different content for the query earthquake before the Haitian earthquake than they did after). In this paper, we explore how queries, their associated documents, and the query intent change over the course of 10 weeks by analyzing query log data, a daily Web crawl, and periodic human relevance judgments. We identify several interesting features by which changes to query popularity can be classified, and show that presence of these features, when accompanied by changes in result content, can be a good indicator of change in query intent.
---
paper_title: Estimating frequency of change

paper_content:

Many online data sources are updated autonomously and independently. In this article, we make the case for estimating the change frequency of data to improve Web crawlers, Web caches and to help data mining. We first identify various scenarios, where different applications have different requirements on the accuracy of the estimated frequency. Then we develop several "frequency estimators" for the identified scenarios, showing analytically and experimentally how precise they are. In many cases, our proposed estimators predict change frequencies much more accurately and improve the effectiveness of applications. For example, a Web crawler could achieve 35p improvement in "freshness" simply by adopting our proposed estimator.
---
paper_title: Web Structure, Dynamics and Page Quality

paper_content:

This paper is aimed at the study of quantitative measures of the relation between Web structure, page recency, and quality of Web pages. Quality is studied using different link-based metrics considering their relationship with the structure of the Web and the last modification time of a page. We show that, as expected, Pagerank is biased against new pages. As a subproduct we propose a Pagerank variant that includes page recency into account and we obtain information on how recency is related with Web structure.
---
paper_title: A large-scale study of the evolution of web pages

paper_content:

How fast does the web change? Does most of the content remain unchanged once it has been authored, or are the documents continuously updated? Do pages change a little or a lot? Is the extent of change correlated to any other property of the page? All of these questions are of interest to those who mine the web, including all the popular search engines, but few studies have been performed to date to answer them.One notable exception is a study by Cho and Garcia-Molina, who crawled a set of 720,000 pages on a daily basis over four months, and counted pages as having changed if their MD5 checksum changed. They found that 40% of all web pages in their set changed within a week, and 23% of those pages that fell into the .com domain changed daily.This paper expands on Cho and Garcia-Molina's study, both in terms of coverage and in terms of sensitivity to change. We crawled a set of 150,836,209 HTML pages once every week, over a span of 11 weeks. For each page, we recorded a checksum of the page, and a feature vector of the words on the page, plus various other data such as the page length, the HTTP status code, etc. Moreover, we pseudo-randomly selected 0.1% of all of our URLs, and saved the full text of each download of the corresponding pages.After completion of the crawl, we analyzed the degree of change of each page, and investigated which factors are correlated with change intensity. We found that the average degree of change varies widely across top-level domains, and that larger pages change more often and more severely than smaller ones.This paper describes the crawl and the data transformations we performed on the logs, and presents some statistical observations on the degree of change of different classes of pages.
---
paper_title: Evaluating methods to rediscover missing web pages from the web infrastructure

paper_content:

Missing web pages (pages that return the 404 "Page Not Found" error) are part of the browsing experience. The manual use of search engines to rediscover missing pages can be frustrating and unsuccessful. We compare four automated methods for rediscovering web pages. We extract the page's title, generate the page's lexical signature (LS), obtain the page's tags from the bookmarking website delicious.com and generate a LS from the page's link neighborhood. We use the output of all methods to query Internet search engines and analyze their retrieval performance. Our results show that both LSs and titles perform fairly well with over 60% URIs returned top ranked from Yahoo!. However, the combination of methods improves the retrieval performance. Considering the complexity of the LS generation, querying the title first and in case of insufficient results querying the LSs second is the preferable setup. This combination accounts for more than 75% top ranked URIs.
---
paper_title: Leveraging temporal dynamics of document content in relevance ranking

paper_content:

Many web documents are dynamic, with content changing in varying amounts at varying frequencies. However, current document search algorithms have a static view of the document content, with only a single version of the document in the index at any point in time. In this paper, we present the first published analysis of using the temporal dynamics of document content to improve relevance ranking. We show that there is a strong relationship between the amount and frequency of content change and relevance. We develop a novel probabilistic document ranking algorithm that allows differential weighting of terms based on their temporal characteristics. By leveraging such content dynamics we show significant performance improvements for navigational queries.
---
paper_title: A survey on web archiving initiatives

paper_content:

Web archiving has been gaining interest and recognized importance for modern societies around the world. However, for web archivists it is frequently difficult to demonstrate this fact, for instance, to funders. This study provides an updated and global overview of web archiving. The obtained results showed that the number of web archiving initiatives significantly grew after 2003 and they are concentrated on developed countries. We statistically analyzed metrics, such as, the volume of archived data, archive file formats or number of people engaged. Web archives all together must process more data than any web search engine. Considering the complexity and large amounts of data involved in web archiving, the results showed that the assigned resources are scarce. A Wikipedia page was created to complement the presented work and be collaboratively kept up-to-date by the community.
---
paper_title: Temporal Evolution of the UK Web

paper_content:

Recently, a new temporal dataset has been made public: it is made of a series of twelve 100 M pages snapshots of the .uk domain. The Web graphs of the twelve snapshots have been merged into a single time-aware graph that provide constant-time access to temporal information. In this paper we present the first statistical analysis performed on this graph, with the goal of checking whether the information contained in the graph is reliable (i.e. whether it depends essentially on appearance and disappearance of pages and links, or on the crawler behaviour). We perform a number of tests that show that the graph is actually reliable, and provide the first public data on the evolution of the Web that use a large scale and a significant diversity in the sites considered.
---
paper_title: Honto? search: estimating trustworthiness of web information by search results aggregation and temporal analysis

paper_content:

If the user wants to know trustworthiness of a proposition, such as whether gthe Japanese Prime Minister is Junichiro Koizumih is true or false, conventional search engines are not appropriate. We therefore propose a system that helps the user to determine trustworthiness of a statement that he or she is unconfident about. In our research, we estimate trustworthiness of a proposition by aggregating knowledge from the Web and analyzing creation time of web pages. We propose a method to estimate popularity from temporal viewpoint by analyzing how many pages discussed the proposition in a certain period of time and how continuously it appeared on the Web.
---
paper_title: A time machine for text search

paper_content:

Text search over temporally versioned document collections such as web archives has received little attention as a research problem. As a consequence, there is no scalable and principled solution to search such a collection as of a specified time. In this work, we address this shortcoming and propose an efficient solution for time-travel text search by extending the inverted file index to make it ready for temporal search. We introduce approximate temporal coalescing as a tunable method to reduce the index size without significantly affecting the quality of results. In order to further improve the performance of time-travel queries, we introduce two principled techniques to trade off index size for its performance. These techniques can be formulated as optimization problems that can be solved to near-optimality. Finally, our approach is evaluated in a comprehensive series of experiments on two large-scale real-world datasets. Results unequivocally show that our methods make it possible to build an efficient "time machine" scalable to large versioned text collections.
---
paper_title: Inverted files for text search engines

paper_content:

The technology underlying text search engines has advanced dramatically in the past decade. The development of a family of new index representations has led to a wide range of innovations in index storage, index construction, and query evaluation. While some of these developments have been consolidated in textbooks, many specific techniques are not widely known or the textbook descriptions are out of date. In this tutorial, we introduce the key techniques in the area, describing both a core implementation and how the core can be enhanced through a range of extensions. We conclude with a comprehensive bibliography of text indexing literature.
---
paper_title: Index maintenance for time-travel text search

paper_content:

Time-travel text search enriches standard text search by temporal predicates, so that users of web archives can easily retrieve document versions that are considered relevant to a given keyword query and existed during a given time interval. Different index structures have been proposed to efficiently support time-travel text search. None of them, however, can easily be updated as the Web evolves and new document versions are added to the web archive. In this work, we describe a novel index structure that efficiently supports time-travel text search and can be maintained incrementally as new document versions are added to the web archive. Our solution uses a sharded index organization, bounds the number of spuriously read index entries per shard, and can be maintained using small in-memory buffers and append-only operations. We present experiments on two large-scale real-world datasets demonstrating that maintaining our novel index structure is an order of magnitude more efficient than periodically rebuilding one of the existing index structures, while query-processing performance is not adversely affected.
---
paper_title: Towards temporal web search

paper_content:

This paper shifts the focus of Web search towards finding and exploiting small text nuggets, rather than full-length documents, assumming that the type of targeted information (e.g., date) is specified in the queries. Each nugget is a document sentence fragment that encodes open-domain factual information associated to some entity. The entities are dates (e.g., 1971) when the events captured by the nuggets must have occurred. The nuggets are extracted from the unstructured text of Web documents, based on lightweight text processing. Since per-document time stamps are available only in restricted collections such as news articles, but not in arbitrary Web documents, the extraction of the relevant dates relies on simple, local text analysis. The resulting time-stamped text nuggets have immediate applications in Web search, as direct results for queries asking about the date of an event explicitly (e.g., "When was the transistor invented") or implicitly (e.g., "Golden Gate Bridge built").
---
paper_title: TISE: A Temporal Search Engine for Web Contents

paper_content:

In this paper, we present a temporal search engine supporting content time retrieval for Web pages, which is called TISE. The main purpose of TISE is to support the Web search on temporal information embedded in Web pages. Compared with commercial search engines such as Google and Baidu, and other temporal search prototypes, which mainly focus on the creation or crawled time of Web pages, our system concentrates on the extraction and search on content time of Web pages, and can provide more meaningful time-based search facilities, such as temporal relation query. In detail, TISE is based on a unified temporal ontology of Web pages, in which different types of time are defined. We introduce a new type of time ldquoprimary timerdquo to denote the most appropriate time describing the content of a Web page. After an overview of the general features of TISE, we discuss the architecture of TISE and some key modules. And finally, experiment results and analysis is conducted based on fix types of temporal-text queries on TISE and www.baidu.com. The experiment results show that TISE is more efficient when processing temporal-text Web queries.
---
paper_title: FluxCapacitor: Efficient Time-Travel Text Search

paper_content:

An increasing number of temporally versioned text collections is available today with Web archives being a prime example. Search on such collections, however, is often not satisfactory and ignores their temporal dimension completely. Time-travel text search solves this problem by evaluating a keyword query on the state of the text collection as of a user-specified time point. This work demonstrates our approach to efficient time-travel text search and its implementation in the FLUXCAPACITOR prototype.
---
paper_title: How fresh do you want your search results?

paper_content:

Researchers have recognized the importance of utilizing temporal features for improving the performance of information retrieval systems. Specifically, the timeliness of a web document can be a significant factor for determining whether it is relevant for a search query. Previous works have proposed time-aware retrieval models with particular focus on news queries, where recent web documents related with a real-world event are generally preferable. These queries typically exhibit bursts in the volume of published documents or submitted queries. However, no work has studied the role of time in queries such as "credit card overdraft fees" that have no major spikes in either document or query volumes over time, yet they still favor more recently published documents. In this work, we focus on this class of queries that we refer to as "timely queries". We show that the change in the terms distribution of results of timely queries over time is strongly correlated with the users' perception of time sensitivity. Based on this observation, we propose a method to estimate the query timeliness requirements and we propose principled ways to incorporate document freshness into the ranking model. Our study shows that our method yields a more accurate estimation of timeliness compared to volume-based approaches. We experimentally compare our ranking strategy with other time-sensitive and non time-sensitive ranking algorithms and we show that it improves the results' retrieval quality for timely queries.
---
paper_title: Integration of news content into web results

paper_content:

Aggregated search refers to the integration of content from specialized corpora or verticals into web search results. Aggregation improves search when the user has vertical intent but may not be aware of or desire vertical search. In this paper, we address the issue of integrating search results from a news vertical into web search results. News is particularly challenging because, given a query, the appropriate decision---to integrate news content or not---changes with time. Our system adapts to news intent in two ways. First, by inspecting the dynamics of the news collection and query volume, we can track development of and interest in topics. Second, by using click feedback, we can quickly recover from system errors. We define several click-based metrics which allow a system to be monitored and tuned without annotator effort.
---
paper_title: Answering general time sensitive queries

paper_content:

Time is an important dimension of relevance for a large number of searches, such as over blogs and news archives. So far, research on searching over such collections has largely focused on locating topically similar documents for a query. Unfortunately, topic similarity alone is not always sufficient for document ranking. In this paper, we observe that, for an important class of queries that we call time-sensitive queries, the publication time of the documents in a news archive is important and should be considered in conjunction with the topic similarity to derive the final document ranking. Earlier work has focused on improving retrieval for "recency" queries that target recent documents. We propose a more general framework for handling time-sensitive queries and we automatically identify the important time intervals that are likely to be of interest for a query. Then, we build scoring techniques that seamlessly integrate the temporal aspect into the overall ranking mechanism. We extensively evaluated our techniques using a variety of news article data sets, including TREC data as well as real web data analyzed using the Amazon Mechanical Turk. We examined several alternatives for detecting the important time intervals for a query over a news archive and for incorporating this information in the retrieval process. Our techniques are robust and significantly improve result quality for time-sensitive queries compared to state-of-the-art retrieval techniques.
---
paper_title: Towards recency ranking in web search

paper_content:

In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detects and responds to recency sensitive queries. The system detects recency sensitive queries using a high precision classifier. The system responds to recency sensitive queries by using a machine learned ranking model trained for such queries. We use multiple recency features to provide temporal evidence which effectively represents document recency. Furthermore, we propose several training methodologies important for training recency sensitive rankers. Finally, we develop new evaluation metrics for recency sensitive queries. Our experiments demonstrate the efficacy of the proposed approaches.
---
paper_title: Estimation methods for ranking recent information

paper_content:

Temporal aspects of documents can impact relevance for certain kinds of queries. In this paper, we build on earlier work of modeling temporal information. We propose an extension to the Query Likelihood Model that incorporates query-specific information to estimate rate parameters, and we introduce a temporal factor into language model smoothing and query expansion using pseudo-relevance feedback. We evaluate these extensions using a Twitter corpus and two newspaper article collections. Results suggest that, compared to prior approaches, our models are more effective at capturing the temporal variability of relevance associated with some topics.
---
paper_title: Click-through prediction for news queries

paper_content:

A growing trend in commercial search engines is the display of specialized content such as news, products, etc. interleaved with web search results. Ideally, this content should be displayed only when it is highly relevant to the search query, as it competes for space with "regular" results and advertisements. One measure of the relevance to the search query is the click-through rate the specialized content achieves when displayed; hence, if we can predict this click-through rate accurately, we can use this as the basis for selecting when to show specialized content. In this paper, we consider the problem of estimating the click-through rate for dedicated news search results. For queries for which news results have been displayed repeatedly before, the click-through rate can be tracked online; however, the key challenge for which previously unseen queries to display news results remains. In this paper we propose a supervised model that offers accurate prediction of news click-through rates and satisfies the requirement of adapting quickly to emerging news events.
---
paper_title: Identifying similarities, periodicities and bursts for online search queries

paper_content:

We present several methods for mining knowledge from the query logs of the MSN search engine. Using the query logs, we build a time series for each query word or phrase (e.g., 'Thanksgiving' or 'Christmas gifts') where the elements of the time series are the number of times that a query is issued on a day. All of the methods we describe use sequences of this form and can be applied to time series data generally. Our primary goal is the discovery of semantically similar queries and we do so by identifying queries with similar demand patterns. Utilizing the best Fourier coefficients and the energy of the omitted components, we improve upon the state-of-the-art in time-series similarity matching. The extracted sequence features are then organized in an efficient metric tree index structure. We also demonstrate how to efficiently and accurately discover the important periods in a time-series. Finally we propose a simple but effective method for identification of bursts (long or short-term). Using the burst information extracted from a sequence, we are able to efficiently perform 'query-by-burst' on the database of time-series. We conclude the presentation with the description of a tool that uses the described methods, and serves as an interactive exploratory data discovery tool for the MSN query database.
---
paper_title: Temporal profiles of queries

paper_content:

Documents with timestamps, such as email and news, can be placed along a timeline. The timeline for a set of documents returned in response to a query gives an indication of how documents relevant to that query are distributed in time. Examining the timeline of a query result set allows us to characterize both how temporally dependent the topic is, as well as how relevant the results are likely to be. We outline characteristic patterns in query result set timelines, and show experimentally that we can automatically classify documents into these classes. We also show that properties of the query result set timeline can help predict the mean average precision of a query. These results show that meta-features associated with a query can be combined with text retrieval techniques to improve our understanding and treatment of text search on documents with timestamps.
---
paper_title: Detecting seasonal queries by time-series analysis

paper_content:

Seasonal events such as Halloween and Christmas repeat every year and initiate several temporal information needs. The impact of such events on users is often reflected in search logs in form of seasonal spikes in the frequency of related queries (e.g. "halloween costumes", "where is santa"). Many seasonal queries such as "sigir conference" mainly target fresh pages (e.g. sigir2011.org) that have less usage data such as clicks and anchor-text compared to older alternatives (e.g.sigir2009.org). Thus, it is important for search engines to correctly identify seasonal queries and make sure that their results are temporally reordered if necessary. In this poster, we focus on detecting seasonal queries using time-series analysis. We demonstrate that the seasonality of a query can be determined with high accuracy according to its historical frequency distribution.
---
paper_title: Time-sensitive query auto-completion

paper_content:

Query auto-completion (QAC) is a common feature in modern search engines. High quality QAC candidates enhance search experience by saving users time that otherwise would be spent on typing each character or word sequentially. Current QAC methods rank suggestions according to their past popularity. However, query popularity changes over time, and the ranking of candidates must be adjusted accordingly. For instance, while halloween might be the right suggestion after typing ha in October, harry potter might be better any other time. Surprisingly, despite the importance of QAC as a key feature in most online search engines, its temporal dynamics have been under-studied. In this paper, we propose a time-sensitive approach for query auto-completion. Instead of ranking candidates according to their past popularity, we apply time-series and rank candidates according their forecasted frequencies. Our experiments on 846K queries and their daily frequencies sampled over a period of 4.5 years show that predicting the popularity of queries solely based on their past frequency can be misleading, and the forecasts obtained by time-series modeling are substantially more reliable. Our results also suggest that modeling the temporal trends of queries can significantly improve the ranking of QAC candidates.
---
paper_title: Determining Time of Queries for Re-ranking Search Results ⋆

paper_content:

Recent work on analyzing query logs shows that a significant fraction of queries are temporal, i.e., relevancy is dependent on time, and temporal queries play an important role in many domains, e.g., digital libraries and document archives. Temporal queries can be divided into two types: 1) those with temporal criteria explicitly provided by users, and 2) those with no temporal criteria provided. In this paper, we deal with the latter type of queries, i.e., queries that comprise only keywords, and their relevant documents are associated to particular time periods not given by the queries. We propose a number of methods to determine the time of queries using temporal language models. After that, we show how to increase the retrieval effectiveness by using the determined time of queries to re-rank the search results. Through extensive experiments we show that our proposed approaches improve retrieval effectiveness.
---
paper_title: Recency ranking by diversification of result set

paper_content:

In this paper, we propose a web search retrieval approach which automatically detects recency sensitive queries and increases the freshness of the ordinary document ranking by a degree proportional to the probability of the need in recent content. We propose to solve the recency ranking problem by using result diversification principles and deal with the query's non-topical ambiguity appearing when the need in recent content can be detected only with uncertainty. Our offine and online experiments with millions of queries from real search engine users demonstrate the significant increase in satisfaction of users presented with a search result generated by our approach.
---
paper_title: Learning Recurrent Event Queries for Web Search

paper_content:

Recurrent event queries (REQ) constitute a special class of search queries occurring at regular, predictable time intervals. The freshness of documents ranked for such queries is generally of critical importance. REQ forms a significant volume, as much as 6% of query traffic received by search engines. In this work, we develop an improved REQ classifier that could provide significant improvements in addressing this problem. We analyze REQ queries, and develop novel features from multiple sources, and evaluate them using machine learning techniques. From historical query logs, we develop features utilizing query frequency, click information, and user intent dynamics within a search session. We also develop temporal features by time series analysis from query frequency. Other generated features include word matching with recurrent event seed words and time sensitivity of search result set. We use Naive Bayes, SVM and decision tree based logistic regression model to train REQ classifier. The results on test data show that our models outperformed baseline approach significantly. Experiments on a commercial Web search engine also show significant gains in overall relevance, and thus overall user experience.
---
paper_title: How fresh do you want your search results?

paper_content:

Researchers have recognized the importance of utilizing temporal features for improving the performance of information retrieval systems. Specifically, the timeliness of a web document can be a significant factor for determining whether it is relevant for a search query. Previous works have proposed time-aware retrieval models with particular focus on news queries, where recent web documents related with a real-world event are generally preferable. These queries typically exhibit bursts in the volume of published documents or submitted queries. However, no work has studied the role of time in queries such as "credit card overdraft fees" that have no major spikes in either document or query volumes over time, yet they still favor more recently published documents. In this work, we focus on this class of queries that we refer to as "timely queries". We show that the change in the terms distribution of results of timely queries over time is strongly correlated with the users' perception of time sensitivity. Based on this observation, we propose a method to estimate the query timeliness requirements and we propose principled ways to incorporate document freshness into the ranking model. Our study shows that our method yields a more accurate estimation of timeliness compared to volume-based approaches. We experimentally compare our ranking strategy with other time-sensitive and non time-sensitive ranking algorithms and we show that it improves the results' retrieval quality for timely queries.
---
paper_title: Time Sensitive Ranking with Application to Publication Search

paper_content:

Link-based ranking has contributed significantly to the success of Web search. PageRank and HITS are the best known link-based ranking algorithms. These algorithms do not consider an important dimension, the temporal dimension. They favor older pages because these pages have many in-links accumulated over time. Bringing new and quality pages to the users is important because most users want the latest information. Existing remedies to PageRank are mostly heuristic approaches. This paper investigates the temporal aspect of ranking with application to publication search, and proposes a principled method based on the stationary probability distribution of the Markov chain. The proposed techniques are evaluated empirically using a large collection of high energy particle physics publication. The results show that the proposed methods are highly effective.
---
paper_title: Time is of the essence: improving recency ranking using Twitter data

paper_content:

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.
---
paper_title: Towards recency ranking in web search

paper_content:

In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detects and responds to recency sensitive queries. The system detects recency sensitive queries using a high precision classifier. The system responds to recency sensitive queries by using a machine learned ranking model trained for such queries. We use multiple recency features to provide temporal evidence which effectively represents document recency. Furthermore, we propose several training methodologies important for training recency sensitive rankers. Finally, we develop new evaluation metrics for recency sensitive queries. Our experiments demonstrate the efficacy of the proposed approaches.
---
paper_title: Estimation methods for ranking recent information

paper_content:

Temporal aspects of documents can impact relevance for certain kinds of queries. In this paper, we build on earlier work of modeling temporal information. We propose an extension to the Query Likelihood Model that incorporates query-specific information to estimate rate parameters, and we introduce a temporal factor into language model smoothing and query expansion using pseudo-relevance feedback. We evaluate these extensions using a Twitter corpus and two newspaper article collections. Results suggest that, compared to prior approaches, our models are more effective at capturing the temporal variability of relevance associated with some topics.
---
paper_title: Learning to rank for freshness and relevance

paper_content:

Freshness of results is important in modern web search. Failing to recognize the temporal aspect of a query can negatively affect the user experience, and make the search engine appear stale. While freshness and relevance can be closely related for some topics (e.g., news queries), they are more independent in others (e.g., time insensitive queries). Therefore, optimizing one criterion does not necessarily improve the other, and can even do harm in some cases. We propose a machine-learning framework for simultaneously optimizing freshness and relevance, in which the trade-off is automatically adaptive to query temporal characteristics. We start by illustrating different temporal characteristics of queries, and the features that can be used for capturing these properties. We then introduce our supervised framework that leverages the temporal profile of queries (inferred from pseudo-feedback documents) along with the other ranking features to improve both freshness and relevance of search results. Our experiments on a large archival web corpus demonstrate the efficacy of our techniques.
---
paper_title: Page quality: in search of an unbiased web ranking

paper_content:

In a number of recent studies [4, 8] researchers have found that because search engines repeatedly return currently popular pages at the top of search results, popular pages tend to get even more popular, while unpopular pages get ignored by an average user. This "rich-get-richer" phenomenon is particularly problematic for new and high-quality pages because they may never get a chance to get users' attention, decreasing the overall quality of search results in the long run. In this paper, we propose a new ranking function, called page quality that can alleviate the problem of popularity-based ranking. We first present a formal framework to study the search engine bias by discussing what is an "ideal" way to measure the intrinsic quality of a page. We then compare how PageRank, the current ranking metric used by major search engines, differs from this ideal quality metric. This framework will help us investigate the search engine bias in more concrete terms and provide clear understanding why PageRank is effective in many cases and exactly when it is problematic. We then propose a practical way to estimate the intrinsic page quality to avoid the inherent bias of PageRank. We derive our proposed quality estimator through a careful analysis of a reasonable web user model, and we present experimental results that show the potential of our proposed estimator. We believe that our quality estimator has the potential to alleviate the rich-get-richer phenomenon and help new and high-quality pages get the attention that they deserve.
---
paper_title: Time-based language models

paper_content:

We explore the relationship between time and relevance using TREC ad-hoc queries. A type of query is identified that favors very recent documents. We propose a time-based language model approach to retrieval for these queries. We show how time can be incorporated into both query-likelihood models and relevance models. These models were used for experiments comparing time-based language models to heuristic techniques for incorporating document recency in the ranking. Our results show that time-based models perform as well as or better than the best of the heuristic techniques.
---
paper_title: A Language Modeling Approach to Information Retrieval

paper_content:

In today's world, there is no shortage of information. However, for a specific information need, only a small subset of all of the available information will be useful. The field of information retrieval (IR) is the study of methods to provide users with that small subset of information relevant to their needs and to do so in a timely fashion. Information sources can take many forms, but this thesis will focus on text based information systems and investigate problems germane to the retrieval of written natural language documents. ::: ::: Central to these problems is the notion of "topic." In other words, what are documents about? However, topics depend on the semantics of documents and retrieval systems are not endowed with knowledge of the semantics of natural language. The approach taken in this thesis will be to make use of probabilistic language models to investigate text based information retrieval and related problems. ::: ::: One such problem is the prediction of topic shifts in text, the topic segmentation problem. It will be shown that probabilistic methods can be used to predict topic changes in the context of the task of new event detection. Two complementary sets of features are studied individually and then combined into a single language model. The language modeling approach allows this problem to be approached in a principled way without complex semantic modeling. ::: ::: Next, the problem of document retrieval in response to a user query will be investigated. Models of document indexing and document retrieval have been extensively studied over the past three decades. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. Much of the reason for this is that the indexing component requires inferences as to the semantics of documents. Instead, an approach to retrieval based on probabilistic language modeling will be presented. Models are estimated for each document individually. The approach to modeling is non-parametric and integrates the entire retrieval process into a single model. One advantage of this approach is that collection statistics, which are used heuristically for the assignment of concept probabilities in other probabilistic models, are used directly in the estimation of language model probabilities in this approach. The language modeling approach has been implemented and tested empirically and performs very well on standard test collections and query sets. ::: ::: In order to improve retrieval effectiveness, IR systems use additional techniques such as relevance feedback, unsupervised query expansion and structured queries. These and other techniques are discussed in terms of the language modeling approach and empirical results are given for several of the techniques developed. These results provide further proof of concept for the use of language models for retrieval tasks.
---
paper_title: Recency ranking by diversification of result set

paper_content:

In this paper, we propose a web search retrieval approach which automatically detects recency sensitive queries and increases the freshness of the ordinary document ranking by a degree proportional to the probability of the need in recent content. We propose to solve the recency ranking problem by using result diversification principles and deal with the query's non-topical ambiguity appearing when the need in recent content can be detected only with uncertainty. Our offine and online experiments with millions of queries from real search engine users demonstrate the significant increase in satisfaction of users presented with a search result generated by our approach.
---
paper_title: Ranking related news predictions

paper_content:

We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
---
paper_title: Clustering and exploring search results using timeline constructions

paper_content:

Time is an important dimension of any information space and can be very useful in information retrieval and in particular clustering and exploration of search results. Search result clustering is a feature integrated in some of today's search engines, allowing users to further explore search results. However, only little work has been done on exploiting temporal information embedded in documents for the presentation, clustering, and exploration of search results along well-defined timelines. In this paper, we present an add-on to traditional information retrieval applications in which we exploit various temporal information associated with documents to present and cluster documents along timelines. Temporal information expressed in the form of, e.g., date and time tokens or temporal references, appear in documents as part of the textual context or metadata. Using temporal entity extraction techniques, we show how temporal expressions are made explicit and used in the construction of multiple-granularity timelines. We discuss how hit-list based search results can be clustered according to temporal aspects, anchored in the constructed timelines, and how time-based document clusters can be used to explore search results that include temporal snippets. We also outline a prototypical implementation and evaluation that demonstrates the feasibility and functionality of our framework.
---
paper_title: A language modeling approach for temporal information needs

paper_content:

This work addresses information needs that have a temporal dimension conveyed by a temporal expression in the user’s query. Temporal expressions such as “in the 1990s” are frequent, easily extractable, but not leveraged by existing retrieval models. One challenge when dealing with them is their inherent uncertainty. It is often unclear which exact time interval a temporal expression refers to. ::: ::: We integrate temporal expressions into a language modeling approach, thus making them first-class citizens of the retrieval model and considering their inherent uncertainty. Experiments on the New York Times Annotated Corpus using Amazon Mechanical Turk to collect queries and obtain relevance assessments demonstrate that our approach yields substantial improvements in retrieval effectiveness.
---
paper_title: The Anatomy of a Large-Scale Hypertextual Web Search Engine

paper_content:

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from 3years ago. This paper provides an in-depth description of our large-scale web search engine - the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections, where anyone can publish anything they want.
---
paper_title: Learning to rank search results for time-sensitive queries

paper_content:

Retrieval effectiveness of temporal queries can be improved by taking into account the time dimension. Existing temporal ranking models follow one of two main approaches: 1) a mixture model linearly combining textual similarity and temporal similarity, and 2) a probabilistic model generating a query from the textual and temporal part of document independently. In this paper, we propose a novel time-aware ranking model based on learning-to-rank techniques. We employ two classes of features for learning a ranking model, entity-based and temporal features, which are derived from annotation data. Entity-based features are aimed at capturing the semantic similarity between a query and a document, whereas temporal features measure the temporal similarity. Through extensive experiments we show that our ranking model significantly improves the retrieval effectiveness over existing time-aware ranking models.
---
paper_title: Towards temporal web search

paper_content:

This paper shifts the focus of Web search towards finding and exploiting small text nuggets, rather than full-length documents, assumming that the type of targeted information (e.g., date) is specified in the queries. Each nugget is a document sentence fragment that encodes open-domain factual information associated to some entity. The entities are dates (e.g., 1971) when the events captured by the nuggets must have occurred. The nuggets are extracted from the unstructured text of Web documents, based on lightweight text processing. Since per-document time stamps are available only in restricted collections such as news articles, but not in arbitrary Web documents, the extraction of the relevant dates relies on simple, local text analysis. The resulting time-stamped text nuggets have immediate applications in Web search, as direct results for queries asking about the date of an event explicitly (e.g., "When was the transistor invented") or implicitly (e.g., "Golden Gate Bridge built").
---
paper_title: Leveraging temporal dynamics of document content in relevance ranking

paper_content:

Many web documents are dynamic, with content changing in varying amounts at varying frequencies. However, current document search algorithms have a static view of the document content, with only a single version of the document in the index at any point in time. In this paper, we present the first published analysis of using the temporal dynamics of document content to improve relevance ranking. We show that there is a strong relationship between the amount and frequency of content change and relevance. We develop a novel probabilistic document ranking algorithm that allows differential weighting of terms based on their temporal characteristics. By leveraging such content dynamics we show significant performance improvements for navigational queries.
---
paper_title: TISE: A Temporal Search Engine for Web Contents

paper_content:

In this paper, we present a temporal search engine supporting content time retrieval for Web pages, which is called TISE. The main purpose of TISE is to support the Web search on temporal information embedded in Web pages. Compared with commercial search engines such as Google and Baidu, and other temporal search prototypes, which mainly focus on the creation or crawled time of Web pages, our system concentrates on the extraction and search on content time of Web pages, and can provide more meaningful time-based search facilities, such as temporal relation query. In detail, TISE is based on a unified temporal ontology of Web pages, in which different types of time are defined. We introduce a new type of time ldquoprimary timerdquo to denote the most appropriate time describing the content of a Web page. After an overview of the general features of TISE, we discuss the architecture of TISE and some key modules. And finally, experiment results and analysis is conducted based on fix types of temporal-text queries on TISE and www.baidu.com. The experiment results show that TISE is more efficient when processing temporal-text Web queries.
---
paper_title: A temporally adaptive content-based relevance ranking algorithm

paper_content:

In information retrieval relevance ranking of the results is one of the most important single tasks there are. There are many diffierent ranking algorithms based on the content of the documents or on some external properties e.g. link structure of html documents.We present a temporally adaptive content-based relevance ranking algorithm that explicitly takes into account the temporal behavior of the underlying statistical properties of the documents in the form of a statistical topic model. more we state that our algorithm can be used on top of any ranking algorithm.
---
paper_title: Determining Time of Queries for Re-ranking Search Results ⋆

paper_content:

Recent work on analyzing query logs shows that a significant fraction of queries are temporal, i.e., relevancy is dependent on time, and temporal queries play an important role in many domains, e.g., digital libraries and document archives. Temporal queries can be divided into two types: 1) those with temporal criteria explicitly provided by users, and 2) those with no temporal criteria provided. In this paper, we deal with the latter type of queries, i.e., queries that comprise only keywords, and their relevant documents are associated to particular time periods not given by the queries. We propose a number of methods to determine the time of queries using temporal language models. After that, we show how to increase the retrieval effectiveness by using the determined time of queries to re-rank the search results. Through extensive experiments we show that our proposed approaches improve retrieval effectiveness.
---
paper_title: Using the past to score the present: extending term weighting models through revision history analysis

paper_content:

The generative process underlies many information retrieval models, notably statistical language models. Yet these models only examine one (current) version of the document, effectively ignoring the actual document generation process. We posit that a considerable amount of information is encoded in the document authoring process, and this information is complementary to the word occurrence statistics upon which most modern retrieval models are based. We propose a new term weighting model, Revision History Analysis (RHA), which uses the revision history of a document (e.g., the edit history of a page in Wikipedia) to redefine term frequency - a key indicator of document topic/relevance for many retrieval models and text processing tasks. We then apply RHA to document ranking by extending two state-of-the-art text retrieval models, namely, BM25 and the generative statistical language model (LM). To the best of our knowledge, our paper is the first attempt to directly incorporate document authoring history into retrieval models. Empirical results show that RHA provides consistent improvements for state-of-the-art retrieval models, using standard retrieval tasks and benchmarks.
---
paper_title: Clustering and exploring search results using timeline constructions

paper_content:

Time is an important dimension of any information space and can be very useful in information retrieval and in particular clustering and exploration of search results. Search result clustering is a feature integrated in some of today's search engines, allowing users to further explore search results. However, only little work has been done on exploiting temporal information embedded in documents for the presentation, clustering, and exploration of search results along well-defined timelines. In this paper, we present an add-on to traditional information retrieval applications in which we exploit various temporal information associated with documents to present and cluster documents along timelines. Temporal information expressed in the form of, e.g., date and time tokens or temporal references, appear in documents as part of the textual context or metadata. Using temporal entity extraction techniques, we show how temporal expressions are made explicit and used in the construction of multiple-granularity timelines. We discuss how hit-list based search results can be clustered according to temporal aspects, anchored in the constructed timelines, and how time-based document clusters can be used to explore search results that include temporal snippets. We also outline a prototypical implementation and evaluation that demonstrates the feasibility and functionality of our framework.
---
paper_title: GTE-Cluster: A Temporal Search Interface for Implicit Temporal Queries

paper_content:

In this paper, we present GTE-Cluster an online temporal search interface which consistently allows searching for topics in a temporal perspective by clustering relevant temporal Web search results. GTE-Cluster is designed to improve user experience by augmenting document relevance with temporal relevance. The rationale is that offering the user a comprehensive temporal perspective of a topic is intuitively more informative than retrieving a result that only contains topical information. Our system does not pose any constraint in terms of language or domain, thus users can issue queries in any language ranging from business, cultural, political to musical perspective, to cite just a few. The ability to exploit this information in a temporal manner can be, from a user perspective, potentially useful for several tasks, including user query understanding or temporal clustering.
---
paper_title: Supporting analysis of future-related information in news archives and the web

paper_content:

A lot of future-related information is available in news articles or Web pages. This information can however differ to large extent and may fluctuate over time. It is therefore difficult for users to manually compare and aggregate it, and to re-construct the most probable course of future events. In this paper we approach a problem of automatically generating summaries of future events related to queries using data obtained from news archive collections or from the Web. We propose two methods, explicit and implicit future-related information detection. The former is based on analyzing the context of future temporal expressions in documents, while the latter relies on detecting periodical patterns in historical document collections. We present a graph-based visualization of future-related information and demonstrate its usefulness through several examples.
---
paper_title: Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets

paper_content:

With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would be interesting to have two separate clusters {1974,2006} corresponding to each of the two temporal instances. However, clustering of search results by time is a non-trivial task that involves determining the most relevant dates associated to a query. In this paper, we propose a first approach to flat temporal clustering of search results. We rely on a second order co-occurrence similarity measure approach which first identifies top relevant dates. Documents are grouped at the year level, forming the temporal instances of the query. Experimental tests were performed using real-world text queries. We used several measures for evaluating the performance of the system and compared our approach with Carrot Web-snippet clustering engine. Both experiments were complemented with a user survey.
---
paper_title: Clustering of search results using temporal attributes

paper_content:

Clustering of search results is an important feature in many of today's information retrieval applications. The notion of hit list clustering appears in Web search engines and enterprise search engines as a mechanism that allows users to further explore the coverage of a query. However, there has been little work on exposing temporal attributes for constructing and presentation of clusters. These attributes appear in documents as part of the textual content, e.g., as a date and time token or as a temporal reference in a sentence. In this paper, we outline a model and describe a prototype that shows the main ideas.
---
paper_title: Reading the correct history?: modeling temporal intention in resource sharing

paper_content:

The web is trapped in the "perpetual now", and when users traverse from page to page, they are seeing the state of the web resource (i.e., the page) as it exists at the time of the click and not necessarily at the time when the link was made. Thus, a temporal discrepancy can arise between the resource at the time the page author created a link to it and the time when a reader follows the link. This is especially important in the context of social media: the ease of sharing links in a tweet or Facebook post allows many people to author web content, but the space constraints combined with poor awareness by authors often prevents sufficient context from being generated to determine the intent of the post. If the links are clicked as soon as they are shared, the temporal distance between sharing and clicking is so small that there is little to no difference in content. However, not all clicks occur immediately, and a delay of days or even hours can result in reading something other than what the author intended. We introduce the concept of a user's temporal intention upon publishing a link in social media. We investigate the features that could be extracted from the post, the linked resource, and the patterns of social dissemination to model this user intention. Finally, we analyze the historical integrity of the shared resources in social media across time. In other words, how much is the knowledge of the author's intent beneficial in maintaining the consistency of the story being told through social posts and in enriching the archived content coverage and depth of vulnerable resources?
---
paper_title: Improving Temporal Language Models For Determining Time of Non-Timestamped Documents

paper_content:

Taking the temporal dimension into account in searching, i.e., using time of content creation as part of the search condition, is now gaining increasingly interest. However, in the case of web search and web warehousing, the timestamps (time of creation or creation of contents) of web pages and documents found on the web are in general not known or can not be trusted, and must be determined otherwise. In this paper, we describe approaches that enhance and increase the quality of existing techniques for determining timestamps based on a temporal language model. Through a number of experiments on temporal document collections we show how our new methods improve the accuracy of timestamping compared to the previous models.
---
paper_title: Clustering and exploring search results using timeline constructions

paper_content:

Time is an important dimension of any information space and can be very useful in information retrieval and in particular clustering and exploration of search results. Search result clustering is a feature integrated in some of today's search engines, allowing users to further explore search results. However, only little work has been done on exploiting temporal information embedded in documents for the presentation, clustering, and exploration of search results along well-defined timelines. In this paper, we present an add-on to traditional information retrieval applications in which we exploit various temporal information associated with documents to present and cluster documents along timelines. Temporal information expressed in the form of, e.g., date and time tokens or temporal references, appear in documents as part of the textual context or metadata. Using temporal entity extraction techniques, we show how temporal expressions are made explicit and used in the construction of multiple-granularity timelines. We discuss how hit-list based search results can be clustered according to temporal aspects, anchored in the constructed timelines, and how time-based document clusters can be used to explore search results that include temporal snippets. We also outline a prototypical implementation and evaluation that demonstrates the feasibility and functionality of our framework.
---
paper_title: Memento: Time Travel for the Web

paper_content:

The Web is ephemeral. Many resources have representations that change over time, and many of those representations are lost forever. A lucky few manage to reappear as archived resources that carry their own URIs. For example, some content management systems maintain version pages that reect a frozen prior state of their changing resources. Archives recurrently crawl the web to obtain the actual representation of resources, and subsequently make those available via special-purpose archived resources. In both cases, the archival copies have URIs that are protocolwise disconnected from the URI of the resource of which they represent a prior state. Indeed, the lack of temporal capabilities in the most common Web protocol, HTTP, prevents getting to an archived resource on the basis of the URI of its original. This turns accessing archived resources into a signicant discovery challenge for both human and software agents, which typically involves following a multitude of links from the original to the archival resource, or of searching archives for the original URI. This paper proposes the protocol-based Memento solution to address this problem, and describes a proof-of-concept experiment that includes major servers of archival content, including Wikipedia and the Internet Archive. The Memento solution is based on existing HTTP capabilities applied in a novel way to add the temporal dimension. The result is a framework in which archived resources can seamlessly be reached via the URI of their original: protocol-based time travel for the Web.
---
paper_title: Using neighbors to date web documents

paper_content:

Time has been successfully used as a feature in web information retrieval tasks. In this context, estimating a document's inception date or last update date is a necessary task. Classic approaches have used HTTP header fields to estimate a document's last update time. The main problem with this approach is that it is applicable to a small part of web documents. In this work, we evaluate an alternative strategy based on a document's neighborhood. Using a random sample containing 10,000 URLs from the Yahoo! Directory, we study each document's links and media assets to determine its age. If we only consider isolated documents, we are able to date 52% of them. Including the document's neighborhood, we are able to estimate the date of more than 86% of the same sample. Also, we find that estimates differ significantly according to the type of neighbors used. The most reliable estimates are based on the document's media assets, while the worst estimates are based on incoming links. These results are experimentally evaluated with a real world application using different datasets.
---
paper_title: Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets

paper_content:

With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would be interesting to have two separate clusters {1974,2006} corresponding to each of the two temporal instances. However, clustering of search results by time is a non-trivial task that involves determining the most relevant dates associated to a query. In this paper, we propose a first approach to flat temporal clustering of search results. We rely on a second order co-occurrence similarity measure approach which first identifies top relevant dates. Documents are grouped at the year level, forming the temporal instances of the query. Experimental tests were performed using real-world text queries. We used several measures for evaluating the performance of the system and compared our approach with Carrot Web-snippet clustering engine. Both experiments were complemented with a user survey.
---
paper_title: Temporal Language Models for the Disclosure of Historical Text

paper_content:

Historical and heritage collections consist for a considerable part of text and may incorporate diverse text types such as journals, archival documents, and catalogue descriptions. Because of the historical distance, access to this content is not straightforward. Historical variants of text are often more complex to identify and retrieve than modern variants. This is due to the less standardized spelling, the effect of on-going language change and different word (de)compounding principles. Moreover, more words are ambiguous because one or more meaning shifts may have occurred. Common full-text search tools can only be applied successfully by users who are able to formulate queries with (a) knowledge of historical language and (b) insight in the relevant time spam from which the words have evolved. This paper explores techniques which may compensate for these linguistic obstacles: linking of contemporary search terms to their historical equivalents and ’dating’ of texts. We envisage to restore the diachronic relationship between terms which may be obscured by language evolution and usage, by applying statistical language models. These models may support the automatic detection of semantic similarities between words and word ambiguities, and they also allow to classify a text according to the time span from which it originates. This approach involves building temporal profiles of words as longitudinal sections in a reference corpus and temporal language models as cross sections. In section 2 some detailed examples will be presented of the added value of this approach both for the accessibility of historical content and the detection of language change in relatively recent corpora from the news domain. In section 3 an overview of related work will be given, plus some technical background on statistical language models. Section 4 describes the proposed methodology in more detail, and some experiments for it in the news domain will described in section 5.
---
paper_title: What's really new on the web?: identifying new pages from a series of unstable web snapshots

paper_content:

Identifying and tracking new information on the Web is important in sociology, marketing, and survey research, since new trends might be apparent in the new information. Such changes can be observed by crawling the Web periodically. In practice, however, it is impossible to crawl the entire expanding Web repeatedly. This means that the novelty of a page remains unknown, even if that page did not exist in previous snapshots. In this paper, we propose a novelty measure for estimating the certainty that a newly crawled page appeared between the previous and current crawls. Using this novelty measure, new pages can be extracted from a series of unstable snapshots for further analysis and mining to identify new trends on the Web. We evaluated the precision, recall, and miss rate of the novelty measure using our Japanese web archive, and applied it to a Web archive search engine.
---
paper_title: Estimating document focus time

paper_content:

Temporality is an important characteristic of text documents. While some documents are clearly atemporal, many have temporal character and can be mapped to certain time periods. In this paper, we introduce the problem of estimating focus time of documents. Document focus time is defined as the time to which the content of a document refers to and is considered as a complementary dimension to its creation time or timestamp. We propose several estimators of focus time by utilizing external knowledge bases such as news article collections which contain explicit temporal references. We then evaluate the effectiveness of our methods on diverse datasets of documents about historical events in five countries.
---
paper_title: Clustering and exploring search results using timeline constructions

paper_content:

Time is an important dimension of any information space and can be very useful in information retrieval and in particular clustering and exploration of search results. Search result clustering is a feature integrated in some of today's search engines, allowing users to further explore search results. However, only little work has been done on exploiting temporal information embedded in documents for the presentation, clustering, and exploration of search results along well-defined timelines. In this paper, we present an add-on to traditional information retrieval applications in which we exploit various temporal information associated with documents to present and cluster documents along timelines. Temporal information expressed in the form of, e.g., date and time tokens or temporal references, appear in documents as part of the textual context or metadata. Using temporal entity extraction techniques, we show how temporal expressions are made explicit and used in the construction of multiple-granularity timelines. We discuss how hit-list based search results can be clustered according to temporal aspects, anchored in the constructed timelines, and how time-based document clusters can be used to explore search results that include temporal snippets. We also outline a prototypical implementation and evaluation that demonstrates the feasibility and functionality of our framework.
---
paper_title: TimeTrails: A System for Exploring Spatio-Temporal Information in Documents

paper_content:

Spatial and temporal data have become ubiquitous in many application domains such as the Geosciences or life sciences. Sophisticated database management systems are employed to manage such structured data. However, an important source of spatio-temporal information that has not been fully utilized are unstructured text documents. In documents, combinations of temporal and spatial expressions form events, which can be mapped to a database structure and organized into trajectories that can be explored. In this context, the coupling of information retrieval techniques with spatio-temporal database concepts leads to new ways for managing and exploring document collections. ::: ::: In this demonstration, we present TimeTrails, a system for the extraction, querying, storage, and exploration of spatio-temporal information embedded in text documents. The user can query a document collection, and TimeTrails visualizes the spatio-temporal information extracted from relevant documents as document trajectories, resulting in a map-based view of documents. This view helps the user to explore the temporal and spatial content of documents in a meaningful way and to further restrict search results using spatial and temporal predicates.
---
paper_title: Handling temporal information in web search engines

paper_content:

TheWeb can be considered a vast repository of temporal information, as it daily receives a huge amount of new pages. Generally, users are interested in information related to a specific temporal interval. In the information retrieval area, researches have newly incorporated the temporal dimension to the search engines. This paper presents a comprehensive study that describes the evolution of search engines on the exploitation of temporal information. Research directions and future perspectives are also presented, considering the authors' point of view.
---
paper_title: GTE-Cluster: A Temporal Search Interface for Implicit Temporal Queries

paper_content:

In this paper, we present GTE-Cluster an online temporal search interface which consistently allows searching for topics in a temporal perspective by clustering relevant temporal Web search results. GTE-Cluster is designed to improve user experience by augmenting document relevance with temporal relevance. The rationale is that offering the user a comprehensive temporal perspective of a topic is intuitively more informative than retrieving a result that only contains topical information. Our system does not pose any constraint in terms of language or domain, thus users can issue queries in any language ranging from business, cultural, political to musical perspective, to cite just a few. The ability to exploit this information in a temporal manner can be, from a user perspective, potentially useful for several tasks, including user query understanding or temporal clustering.
---
paper_title: Disambiguating Implicit Temporal Queries by Clustering Top Relevant Dates in Web Snippets

paper_content:

With the growing popularity of research in Temporal Information Retrieval (T-IR), a large amount of temporal data is ready to be exploited. The ability to exploit this information can be potentially useful for several tasks. For example, when querying "Football World Cup Germany", it would be interesting to have two separate clusters {1974,2006} corresponding to each of the two temporal instances. However, clustering of search results by time is a non-trivial task that involves determining the most relevant dates associated to a query. In this paper, we propose a first approach to flat temporal clustering of search results. We rely on a second order co-occurrence similarity measure approach which first identifies top relevant dates. Documents are grouped at the year level, forming the temporal instances of the query. Experimental tests were performed using real-world text queries. We used several measures for evaluating the performance of the system and compared our approach with Carrot Web-snippet clustering engine. Both experiments were complemented with a user survey.
---
paper_title: Clustering of search results using temporal attributes

paper_content:

Clustering of search results is an important feature in many of today's information retrieval applications. The notion of hit list clustering appears in Web search engines and enterprise search engines as a mechanism that allows users to further explore the coverage of a query. However, there has been little work on exposing temporal attributes for constructing and presentation of clusters. These attributes appear in documents as part of the textual content, e.g., as a date and time token or as a temporal reference in a sentence. In this paper, we outline a model and describe a prototype that shows the main ideas.
---
paper_title: TISE: A Temporal Search Engine for Web Contents

paper_content:

In this paper, we present a temporal search engine supporting content time retrieval for Web pages, which is called TISE. The main purpose of TISE is to support the Web search on temporal information embedded in Web pages. Compared with commercial search engines such as Google and Baidu, and other temporal search prototypes, which mainly focus on the creation or crawled time of Web pages, our system concentrates on the extraction and search on content time of Web pages, and can provide more meaningful time-based search facilities, such as temporal relation query. In detail, TISE is based on a unified temporal ontology of Web pages, in which different types of time are defined. We introduce a new type of time ldquoprimary timerdquo to denote the most appropriate time describing the content of a Web page. After an overview of the general features of TISE, we discuss the architecture of TISE and some key modules. And finally, experiment results and analysis is conducted based on fix types of temporal-text queries on TISE and www.baidu.com. The experiment results show that TISE is more efficient when processing temporal-text Web queries.
---
paper_title: FluxCapacitor: Efficient Time-Travel Text Search

paper_content:

An increasing number of temporally versioned text collections is available today with Web archives being a prime example. Search on such collections, however, is often not satisfactory and ignores their temporal dimension completely. Time-travel text search solves this problem by evaluating a keyword query on the state of the text collection as of a user-specified time point. This work demonstrates our approach to efficient time-travel text search and its implementation in the FLUXCAPACITOR prototype.
---
paper_title: Improving Retrieval of Future-Related Information in Text Collections

paper_content:

People often want to know expected future events related to given real world entities. For supporting users in the process of future scenario analysis, we propose several methods that enable to retrieve and analyze future-related opinions from large text collections. In particular, we focus on time-unreferenced predictions, which do not contain any explicit future time reference and hence are more difficult to be retrieved. As a second contribution, we propose estimating validity of predictions by automatically searching for real world events corresponding to the predictions. This kind of analysis aims to help detect predictions that are no longer valid as well as help estimating prediction accuracy of information sources.
---
paper_title: Ranking related news predictions

paper_content:

We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
---
paper_title: An Exploratory Study on the Impact of Temporal Features on the Classification and Clustering of Future-Related Web Documents

paper_content:

In the last few years, a huge amount of temporal written information has become widely available on the Internet with the advent of forums, blogs and social networks. This gave rise to a new challenging problem called future retrieval, which consists of extracting future temporal information, that is known in advance, from web sources in order to answer queries that combine text of a future temporal nature. This paper aims to confirm whether web snippets can be used to form an intelligent web that can detect future expected events when their dates are already known. Moreover, the objective is to identify the nature of future texts and understand how these temporal features affect the classification and clustering of the different types of future-related texts: informative texts, scheduled texts and rumor texts. We have conducted a set of comprehensive experiments and the results show that web documents are a valuable source of future data that can be particularly useful in identifying and understanding the future temporal nature of a given implicit temporal query.
---
paper_title: Activity Prediction: A Twitter-based Exploration

paper_content:

Social media platforms allow users to share their messages with everyone else. In microblogs, e.g., Twitter, people mostly report on what they did, they talk about current activities, and mention things they plan to do in the near future. In this paper, we propose the task of activity prediction, that is, trying to establish a set of activities that are likely to become popular at a later time. We perform a small-scale initial experiment, in which we try to predict popular activities for the coming evening using Dutch Twitter data. Our experiment shows the feasibility and challenges of the task, with a simple method resulting in human-readable activities. This exploration also identifies several issues (e.g., temporal phrases and activity classification) that need to be addressed in future work.
---
paper_title: Supporting analysis of future-related information in news archives and the web

paper_content:

A lot of future-related information is available in news articles or Web pages. This information can however differ to large extent and may fluctuate over time. It is therefore difficult for users to manually compare and aggregate it, and to re-construct the most probable course of future events. In this paper we approach a problem of automatically generating summaries of future events related to queries using data obtained from news archive collections or from the Web. We propose two methods, explicit and implicit future-related information detection. The former is based on analyzing the context of future temporal expressions in documents, while the latter relies on detecting periodical patterns in historical document collections. We present a graph-based visualization of future-related information and demonstrate its usefulness through several examples.
---
paper_title: Extracting and aggregating temporal events from text

paper_content:

Finding reliable information about a given event from large and dynamic text collections is a topic of great interest. For instance, rescue teams and insurance companies are interested in concise facts about damages after disasters, which can be found in web blogs, newspaper articles, social networks etc. However, finding, extracting, and condensing specific facts is a highly complex undertaking: It requires identifying appropriate textual sources, recognizing relevant facts within the sources, and aggregating extracted facts into a condensed answer despite inconsistencies, uncertainty, and changes over time. In this paper, we present a three-step framework providing techniques and solutions for each of these problems. We tested the feasibility of extracting time-associated event facts using our framework in a comprehensive case study: gathering data on particular earthquakes from web data sources. Our results show that it is, under certain circumstances, possible to automatically obtain reliable and timely data on natural disasters from the web.
---
paper_title: Your Two Weeks of Fame and Your Grandmother's

paper_content:

Did celebrity last longer in 1929, 1992 or 2009? We investigate the phenomenon of fame by mining a collection of news articles that spans the twentieth century, and also perform a side study on a collection of blog posts from the last 10 years. By analyzing mentions of personal names, we measure each person's time in the spotlight, and watch the distribution change from a century ago to a year ago. We expected to find a trend of decreasing durations of fame as news cycles accelerated and attention spans became shorter. Instead, we find a remarkable consistency through most of the period we study. Through a century of rapid technological and societal change, through the appearance of Twitter, communication satellites and the Internet, we do not observe a significant change in typical duration of celebrity. We also study the most famous of the famous, and find different results depending on our method for measuring duration of fame. With a method that may be thought of as measuring a spike of attention around a single narrow news story, we see the same result as before: stories last as long now as they did in 1930. A second method, which may be thought of as measuring the duration of public interest in a person, indicates that famous people's presence in the news is becoming longer rather than shorter, an effect most likely driven by the wider distribution and higher volume of media in modern times. Similar studies have been done with much shorter timescales specifically in the context of information spreading on Twitter and similar social networking site. However, to the best of our knowledge, this is the first massive scale study of this nature that spans over a century of archived data, thereby allowing us to track changes across decades.
---
paper_title: Mining history with Le Monde

paper_content:

The last decade has seen the rise of large knowledge bases, such as YAGO, DBpedia, Freebase, or NELL. In this paper, we show how this structured knowledge can help understand and mine trends in unstructured data. By combining YAGO with the archive of the French newspaper Le Monde, we can conduct analyses that would not be possible with word frequency statistics alone. We find indications about the increasing role that women play in politics, about the impact that the city of birth can have on a person's career, or about the average age of famous people in different professions.
---
paper_title: Improving Retrieval of Future-Related Information in Text Collections

paper_content:

People often want to know expected future events related to given real world entities. For supporting users in the process of future scenario analysis, we propose several methods that enable to retrieve and analyze future-related opinions from large text collections. In particular, we focus on time-unreferenced predictions, which do not contain any explicit future time reference and hence are more difficult to be retrieved. As a second contribution, we propose estimating validity of predictions by automatically searching for real world events corresponding to the predictions. This kind of analysis aims to help detect predictions that are no longer valid as well as help estimating prediction accuracy of information sources.
---
paper_title: Looking back at big data

paper_content:

As computational tools open up new ways of understanding history, historians and computer scientists are working together to explore the possibilities.
---
paper_title: Studying how the past is remembered: towards computational history through large scale text mining

paper_content:

History helps us understand the present and even to predict the future to certain extent. Given the huge amount of data about the past, we believe computer science will play an increasingly important role in historical studies, with computational history becoming an emerging interdisciplinary field of research. We attempt to study how the past is remembered through large scale text mining. We achieve this by first collecting a large dataset of news articles about different countries and analyzing the data using computational and statistical tools. We show that analysis of references to the past in news articles allows us to gain a lot of insight into the collective memories and societal views of different countries. Our work demonstrates how various computational tools can assist us in studying history by revealing interesting topics and hidden correlations. Our ultimate objective is to enhance history writing and evaluation with the help of algorithmic support.
---
paper_title: Wikipedia as a time machine

paper_content:

Wikipedia encyclopaedia projects, which consist of vast collections of user-edited articles covering a wide range of topics, are among some of the most popular websites on internet. With so many users working collaboratively, mainstream events are often very quickly reflected by both authors editing content and users reading articles. With temporal signals such as changing article content, page viewing activity and the link graph readily available, Wikipedia has gained attention in recent years as a source of temporal event information. This paper serves as an overview of the characteristics and past work which support Wikipedia (English, in this case) for time-aware information retrieval research. Furthermore, we discuss the main content and meta-data temporal signals available along with illustrative analysis. We briefly discuss the source and nature of each signal, and any issues that may complicate extraction and use. To encourage further temporal research based on Wikipedia, we have released all the distilled datasets referred to in this paper.
---
paper_title: Quantitative Analysis of Culture Using Millions of Digitized Books

paper_content:

We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.
---
paper_title: Creating temporally dynamic web search snippets

paper_content:

Content on the Internet is always changing. We explore the value of biasing search result snippets towards new webpage content. We present results from a user study comparing traditional query-focused snippets with snippets that emphasize new page content for two query types: general and trending. Our results indicate that searchers prefer the inclusion of temporal information for trending queries but not for general queries, and that this is particularly valuable for pages that have not been recently crawled.
---
paper_title: Temporal Web Image Retrieval

paper_content:

Temporal Web Image Retrieval can be defined as the process that retrieves sets of Web images with their temporal dimension from explicit or implicit temporal text queries. Supposing that (a) the temporal dimension is included in image indexing and (b) the query is explicitly expressed with a time tag (e.g. "Fukushima 2011"), the retrieval task can be straightforward as image retrieval has been studied for several years with success. However, text queries are usually implicit in time (e.g. "Second World War") and automatically capturing the time dimension included in Web images is a challenge that has not been studied so far to the best of our knowledge. In this paper, we will discuss different research issues about Temporal Web Image Retrieval and the current progresses of our research in temporal ephemeral clustering and temporal image filtering.
---
paper_title: Effectiveness of Temporal Snippets

paper_content:

We introduce the notion of time-centered snippets, called TSnippet, as document surrogates for document retrieval and exploration. We propose an alternative document snippet based on temporal information that can be useful for supporting exploratory search. The idea of using sentences that contain the most frequent chronons (units of time) can be used for constructing document surrogates. We conducted a series of experiments to evaluate this new approach using a crowdsourcing approach. The evaluation against two Web search engines shows that our technique produces good snippets and users like to see time-sensitive information in search results.
---
paper_title: Time-sensitive web image ranking and retrieval via dynamic multi-task regression

paper_content:

In this paper, we investigate a time-sensitive image retrieval problem, in which given a query keyword, a query time point, and optionally user information, we retrieve the most relevant and temporally suitable images from the database. Inspired by recently emerging interests on query dynamics in information retrieval research, our time-sensitive image retrieval algorithm can infer users' implicit search intent better and provide more engaging and diverse search results according to temporal trends of Web user photos. We model observed image streams as instances of multivariate point processes represented by several different descriptors, and develop a regularized multi-task regression framework that automatically selects and learns stochastic parametric models to solve the relations between image occurrence probabilities and various temporal factors that influence them. Using Flickr datasets of more than seven million images of 30 topics, our experimental results show that the proposed algorithm is more successful in time-sensitive image retrieval than other candidate methods, including ranking SVM, a PageRank-based image ranking, and a generative temporal topic model.
---
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.
---
paper_title: SparkClouds: Visualizing Trends in Tag Clouds

paper_content:

Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.
---
paper_title: Analyzing temporal characteristics of check-in data

paper_content:

There is a surge in the use of location activity in social media, in particular to broadcast the change of physical whereabouts. We are interested in analyzing the temporal characteristics of check-ins data from the user's perspective and also at the aggregate level for detecting patterns. In this paper we conduct a large study using check-in data from Facebook to analyze different temporal characteristics in four venue categories (restaurants, movies, shopping, and get-away). We present the results of such study and outline application areas where the conjunction of location and temporal-aware data can help in new search scenarios.
---
paper_title: Retrospective Privacy: Managing Longitudinal Privacy in Online Social Networks

paper_content:

Online social networks provide access to the user's information for long periods of time after the information's initial publication. In this paper, we investigate the relation between information aging and its sharing preferences on Facebook. Our findings are based on a survey of 193 Facebook users, in which we asked users to specify their sharing preferences and intentions towards posts that were published in different periods of time (from the time of the survey and up to 24 months prior to the time of the survey.) Our results show that willingness to share significantly drops with the time passed since publishing the post. The occurrence of life changes, such as graduating from college or moving to a new town, is correlated with a further decrease in the willingness to share. We discuss our findings by relating it to information aging theories and privacy theories. Finally, we use our results to reflect on privacy mechanisms for long-term usage of online social networks, such as expiry date for content and historical information reviewing processes.
---
paper_title: A word at a time: Computing word relatedness using temporal semantic analysis

paper_content:

Computing the degree of semantic relatedness of words is a key functionality of many language applications such as search, clustering, and disambiguation. Previous approaches to computing semantic relatedness mostly used static language resources, while essentially ignoring their temporal aspects. We believe that a considerable amount of relatedness information can also be found in studying patterns of word usage over time. Consider, for instance, a newspaper archive spanning many years. Two words such as "war" and "peace" might rarely co-occur in the same articles, yet their patterns of use over time might be similar. In this paper, we propose a new semantic relatedness model, Temporal Semantic Analysis (TSA), which captures this temporal information. The previous state of the art method, Explicit Semantic Analysis (ESA), represented word semantics as a vector of concepts. TSA uses a more refined representation, where each concept is no longer scalar, but is instead represented as time series over a corpus of temporally-ordered documents. To the best of our knowledge, this is the first attempt to incorporate temporal evidence into models of semantic relatedness. Empirical evaluation shows that TSA provides consistent improvements over the state of the art ESA results on multiple benchmarks.
---
paper_title: Wikipedia as a time machine

paper_content:

Wikipedia encyclopaedia projects, which consist of vast collections of user-edited articles covering a wide range of topics, are among some of the most popular websites on internet. With so many users working collaboratively, mainstream events are often very quickly reflected by both authors editing content and users reading articles. With temporal signals such as changing article content, page viewing activity and the link graph readily available, Wikipedia has gained attention in recent years as a source of temporal event information. This paper serves as an overview of the characteristics and past work which support Wikipedia (English, in this case) for time-aware information retrieval research. Furthermore, we discuss the main content and meta-data temporal signals available along with illustrative analysis. We briefly discuss the source and nature of each signal, and any issues that may complicate extraction and use. To encourage further temporal research based on Wikipedia, we have released all the distilled datasets referred to in this paper.
---
paper_title: The post anachronism: the temporal dimension of facebook privacy

paper_content:

This paper reports on two studies that investigate empirically how privacy preferences about the audience and emphasis of Facebook posts change over time. In a 63-participant longitudinal study, participants gave their audience and emphasis preferences for up to ten of their Facebook posts in the week they were posted, again one week later, and again one month later. In a 234-participant retrospective study, participants expressed their preferences about posts made in the past week, as well as one year prior. We found that participants did not want content to fade away wholesale with age; the audience participants wanted to be able to access posts remained relatively constant over time. However, participants did want a handful of posts to become more private over time, as well as others to become more visible. Participants' predictions about how their preferences would change correlated poorly with their actual changes in preferences over time, casting doubt on ideas for setting an expiration date for content. Although older posts were seen as less relevant and had often been forgotten, participants found value in these posts for reminiscence. Surprisingly, we observed few concerns about privacy or self-presentation for older posts. We discuss our findings' implications for retrospective privacy mechanisms.
---
paper_title: NTCIR temporalia: a test collection for temporal information access research

paper_content:

Time is one of the key constructs of information quality. Following an upsurge of research in temporal aspects of information search, it has become clear that the community needs standardized evaluation benchmark for fostering research in Temporal Information Access. This paper introduces Temporalia (Temporal Information Access), a new pilot task run at NTCIR-11 to create re-usable datasets for those who are interested in temporal aspects of search technologies, and discusses its task design in detail.
---
paper_title: The Visual Display of Temporal Information

paper_content:

The detection of temporal relationships among time-ordered patient data is an important, but difficult, clinical task. Large volumes of computer-stored clinical data offer the possibility of aiding in the early detection of subtle trends and states, but the presence of irrelevant data can obscure relevant findings and relationships. We present a formal system for representing complex temporal data as events on an abstract entity called a time line. We define five time line operations, SLICE, FILTER, OVERLAY, NEW, and ADD. For each operation, we precisely define the operator's effect on a time line, including exceptions and boundary conditions. In addition to our time line formalism, we describe an interactive environment designed specifically to help humans visualize temporal data. We have developed a database kernel and a graphical user interface that used our time line formalism and operations to support temporal manipulations. Using our formal system and our visualization environment, we describe two issues in the display and manipulation of temporal data: 1.1. the temporal granularity problem, and 2.2. the calendar mapping problem.
---
paper_title: Tweet classification based on their lifetime duration

paper_content:

Many microblog messages remain useful only within a short time, and users often find such a message after its informational value has vanished. Users also sometimes miss old but still useful messages buried among outdated ones. To solve these problems, we develop a method of classifying messages into the following three categories: (1) messages that users should read now because their value will diminish soon, (2) messages that users may read later because their value will not largely change soon, and (3) messages that are not useful anymore because their value has vanished. Our method uses an error correcting output code consisting of binary classifiers each of which determines whether a given message has value at specific time point. Our experiments on Twitter data confirmed that it outperforms naive methods.
---
paper_title: The Role of Social Networks in Information Diffusion

paper_content:

Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these mediums on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.
---
paper_title: LifeLines: visualizing personal histories

paper_content:

LifeLines provide a general visualization environment for personal histories that can be applied to medical and court records, professional histories and other types of biographical data. A one screen overview shows multiple facets of the records. Aspects, for example medical conditions or legal cases, are displayed as individual time lines, while icons indicate discrete events, such as physician consultations or legal reviews. Line color and thickness illustrate relationships or significance, rescaling tools and filters allow users to focus on part of the information. LifeLines reduce the chances of missing information, facilitate spotting anomalies and trends, streamline access to details, while remaining tailorable and easily transferable between applications. The paper describes the use of LifeLines for youth records of the Maryland Department of Juvenile Justice and also for medical records. User's feedback was collected using a Visual Basic prototype for the youth record. Techniques to deal with complex records are reviewed and issues of a standard personal record format are discussed.
---
paper_title: Dating Historical Color Images

paper_content:

We introduce the task of automatically estimating the age of historical color photographs. We suggest features which attempt to capture temporally discriminative information based on the evolution of color imaging processes over time and evaluate the performance of both these novel features and existing features commonly utilized in other problem domains on a novel historical image data set. For the challenging classification task of sorting historical color images into the decade during which they were photographed, we demonstrate significantly greater accuracy than that shown by untrained humans on the same data set. Additionally, we apply the concept of data-driven camera response function estimation to historical color imagery, demonstrating its relevance to both the age estimation task and the popular application of imitating the appearance of vintage color photography.
---
paper_title: Visualization using timelines

paper_content:

A timeline is a linear, graphical visualization of events over time. For example, in concurrent application, events would represent state changes for some system object (such as a task or variable). A timeline display generator creates the graphical visualization from some record of events. This paper reports on a model for timeline display generators based on a formal model of event history and the objectives of timeline visualization. In this model, any timeline display generator is completely described through the definition of a set of mathematical functions. The exact characteristics and flexibility of a particular implementation of a timeline display generator, depends on the way in which these functions have been implemented. The current prototype, xtg, (Timeline Display Generator for X-windows) serves as an example implementation of these ideas. Characteristics of xtg are presented, and its use in the analysis of a real-world client-server application is discussed. Xtg has been applied to several other applications to-date and is being applied by several telecommunications companies to areas ranging from software process analysis to call trace data analysis.
---
paper_title: The Anatomy of the Facebook Social Graph

paper_content:

We study the structure of the social graph of active Facebook users, the largest social network ever analyzed. We compute numerous features of the graph including the number of users and friendships, the degree distribution, path lengths, clustering, and mixing patterns. Our results center around three main observations. First, we characterize the global structure of the graph, determining that the social network is nearly fully connected, with 99.91% of individuals belonging to a single large connected component, and we confirm the "six degrees of separation" phenomenon on a global scale. Second, by studying the average local clustering coefficient and degeneracy of graph neighborhoods, we show that while the Facebook graph as a whole is clearly sparse, the graph neighborhoods of users contain surprisingly dense structure. Third, we characterize the assortativity patterns present in the graph by studying the basic demographic and network properties of users. We observe clear degree assortativity and characterize the extent to which "your friends have more friends than you". Furthermore, we observe a strong effect of age on friendship preferences as well as a globally modular community structure driven by nationality, but we do not find any strong gender homophily. We compare our results with those from smaller social networks and find mostly, but not entirely, agreement on common structural network characteristics.
---
paper_title: Hashtags as Milestones in Time

paper_content:

On Twitter, hashtags are typically used by authors wishing to explicitly denote the relevant topic(s) contained in their message, especially when the text quantity is limited. Hashtags are widely used by both tweet authors as well as users searching for specific topics. As such, frequently used hashtags often reflect either mainstream events in real-time, or circulating memes. In this paper we propose an approach to identify significant event-based hashtags and use them as annotations for constructing timelines. Such timelines allow easy time-based navigation of hashtagged information. To determine temporally significant hashtags, we correlate the periods of hashtag use in the Bing Social search engine query-log with the periods of high page viewing popularity of the Wikipedia article(s) most related to the words contained in the hashtag. Preliminary results suggest that the technique is effective for hashtags arising from large-scale events, including hurricanes, celebrities, music releases, television shows and sports games.
---

Title: Survey of Temporal Information Retrieval and Related Applications

Section 1: INTRODUCTION
Description 1: Introduce the relevance and challenges of Temporal Information Retrieval (T-IR) and provide background context.

Section 2: MODELS OF TEMPORAL ANNOTATIONS OF DOCUMENTS
Description 2: Discuss the different temporal dimensions and how they are used for temporal annotations in documents.

Section 3: EXTRACTING TEMPORAL INFORMATION FROM WEB RESOURCES
Description 3: Explain the various approaches to extract temporal features from web resources, including metadata, content, and usage methodologies.

Section 4: TEMPORAL WEB INFORMATION RETRIEVAL
Description 4: Explore key advances in web T-IR, detailing different approaches used in the execution of web T-IR processes.

Section 5: FUTURE-RELATED INFORMATION RETRIEVAL
Description 5: Highlight methods and applications for retrieving and processing future-related information.

Section 6: PROMISING RESEARCH DIRECTIONS
Description 6: Identify potential future trends and research directions within the field of temporal information retrieval.

Section 7: CONCLUSION
Description 7: Summarize the key findings of the survey and provide final remarks on the future of T-IR.