TY - CONF AU - Atzmueller, Martin AU - Doerfel, Stephan AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - T1 - Face-to-Face Contacts during a Conference: Communities, Roles, and Key Players T2 - Proc. Workshop on Mining Ubiquitous and Social Environments (MUSE 2011) at ECML/PKDD 2011 PB - CY - PY - 2011/ M2 - VL - IS - SP - EP - UR - M3 - KW - 2011 KW - analysis KW - communities KW - community KW - discovery KW - face KW - itegpub KW - knowledge KW - mining KW - myown KW - rfid KW - venus KW - venuspub L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - An, Yuan AU - Janssen, Jeannette AU - Milios, Evangelos E. T1 - Characterizing and Mining the Citation Graph of the Computer Science Literature JO - Knowl. Inf. Syst. PY - 2004/november VL - 6 IS - SP - 664 EP - 678 UR - http://dx.doi.org/10.1007/s10115-003-0128-3 M3 - http://dx.doi.org/10.1007/s10115-003-0128-3 KW - 10th KW - Citation KW - characterizing KW - citation KW - computer KW - graph KW - mining L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Pak, Alexander AU - Paroubek, Patrick A2 - T1 - Twitter as a Corpus for Sentiment Analysis and Opinion Mining T2 - PB - CY - PY - 2010/ M2 - VL - 2010 IS - SP - 1320 EP - 1326 UR - http://www.mendeley.com/research/twitter-corpus-sentiment-analysis-opinion-mining-18/ M3 - KW - emotion KW - mining KW - seminar KW - sentiment KW - twitter L1 - SN - N1 - Twitter as a Corpus for Sentiment Analysis and Opinion Mining | Mendeley N1 - AB - ER - TY - CHAP AU - Atzmueller, Martin AU - Doerfel, Stephan AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - T1 - Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles T2 - Modeling and Mining Ubiquitous Social Media PB - Springer Verlag CY - Heidelberg, Germany PY - 2012/ VL - 7472 IS - SP - EP - UR - M3 - KW - 2012 KW - conference KW - contacts KW - dynamics KW - face KW - intelligence KW - itegpub KW - kde KW - mining KW - myown KW - social KW - ubiquitous KW - venus KW - venuspub L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Jones, Steve AU - Cunningham, Sally Jo AU - McNab, Rodger A2 - T1 - Usage analysis of a digital library T2 - Proceedings of the third ACM conference on Digital libraries PB - ACM CY - New York, NY, USA PY - 1998/ M2 - VL - IS - SP - 293 EP - 294 UR - http://doi.acm.org/10.1145/276675.276739 M3 - 10.1145/276675.276739 KW - analysis KW - digital KW - library KW - logs KW - mining KW - usage KW - weblog L1 - SN - 0-89791-965-3 N1 - Usage analysis of a digital library N1 - AB - ER - TY - JOUR AU - Nicholas, David AU - Huntington, Paul AU - Watkinson, Anthony T1 - Scholarly journal usage: the results of deep log analysis JO - Journal of Documentation PY - 2005/ VL - 61 IS - 2 SP - 248 EP - 280 UR - http://www.emeraldinsight.com/journals.htm?articleid=1465024&show=abstract M3 - KW - Scholarly KW - analysis KW - deep KW - journal KW - log KW - mining KW - usage KW - weblog L1 - SN - N1 - Emerald | Journal of Documentation | Scholarly journal usage: the results of deep log analysis N1 - AB - ER - TY - JOUR AU - Silverstein, Craig AU - Marais, Hannes AU - Henzinger, Monika AU - Moricz, Michael T1 - Analysis of a very large web search engine query log JO - SIGIR Forum PY - 1999/10 VL - 33 IS - 1 SP - 6 EP - 12 UR - http://doi.acm.org/10.1145/331403.331405 M3 - 10.1145/331403.331405 KW - altavista KW - behaviour KW - engine KW - log KW - mining KW - query KW - search KW - weblog L1 - SN - N1 - Analysis of a very large web search engine query log N1 - AB - In this paper we present an analysis of an AltaVista Search Engine query log consisting of approximately 1 billion entries for search requests over a period of six weeks. This represents almost 285 million user sessions, each an attempt to fill a single information need. We present an analysis of individual queries, query duplication, and query sessions. We also present results of a correlation analysis of the log entries, studying the interaction of terms within queries. Our data supports the conjecture that web users differ significantly from the user assumed in the standard information retrieval literature. Specifically, we show that web users type in short queries, mostly look at the first 10 results only, and seldom modify the query. This suggests that traditional information retrieval techniques may not work well for answering web search requests. The correlation analysis showed that the most highly correlated items are constituents of phrases. This result indicates it may be useful for search engines to consider search terms as parts of phrases even if the user did not explicitly specify them as such. ER - TY - JOUR AU - Jansen, Bernard J. T1 - Search log analysis: What it is, what's been done, how to do it JO - Library & Information Science Research PY - 2006/ VL - 28 IS - 3 SP - 407 EP - 432 UR - http://www.sciencedirect.com/science/article/pii/S0740818806000673 M3 - 10.1016/j.lisr.2006.06.005 KW - analysis KW - howto KW - log KW - mining KW - search KW - weblog L1 - SN - N1 - ScienceDirect.com - Library & Information Science Research - Search log analysis: What it is, what's been done, how to do it N1 - AB - The use of data stored in transaction logs of Web search engines, Intranets, and Web sites can provide valuable insight into understanding the information-searching process of online searchers. This understanding can enlighten information system design, interface development, and devising the information architecture for content collections. This article presents a review and foundation for conducting Web search transaction log analysis. A methodology is outlined consisting of three stages, which are collection, preparation, and analysis. The three stages of the methodology are presented in detail with discussions of goals, metrics, and processes at each stage. Critical terms in transaction log analysis for Web searching are defined. The strengths and limitations of transaction log analysis as a research method are presented. An application to log client-side interactions that supplements transaction logs is reported on, and the application is made available for use by the research community. Suggestions are provided on ways to leverage the strengths of, while addressing the limitations of, transaction log analysis for Web-searching research. Finally, a complete flat text transaction log from a commercial search engine is available as supplementary material with this manuscript. ER - TY - CHAP AU - Baglioni, M. AU - Ferrara, U. AU - Romei, A. AU - Ruggieri, S. AU - Turini, F. A2 - Cappelli, Amedeo A2 - Turini, Franco T1 - Preprocessing and Mining Web Log Data for Web Personalization T2 - AI*IA 2003: Advances in Artificial Intelligence PB - Springer Berlin Heidelberg CY - PY - 2003/ VL - 2829 IS - SP - 237 EP - 249 UR - http://dx.doi.org/10.1007/978-3-540-39853-0_20 M3 - 10.1007/978-3-540-39853-0_20 KW - classification KW - gender KW - interest KW - mining KW - user KW - weblog L1 - SN - 978-3-540-20119-9 N1 - Preprocessing and Mining Web Log Data for Web Personalization - Springer N1 - AB - We describe the web usage mining activities of an on-going project, called ClickWorld, that aims at extracting models of the navigational behaviour of a web site users. The models are inferred from the access logs of a web server by means of data and web mining techniques. The extracted knowledge is deployed to the purpose of offering a personalized and proactive view of the web services to users. We first describe the preprocessing steps on access logs necessary to clean, select and prepare data for knowledge extraction. Then we show two sets of experiments: the first one tries to predict the sex of a user based on the visited web pages, and the second one tries to predict whether a user might be interested in visiting a section of the site. ER - TY - CONF AU - Rose, Daniel E. AU - Levinson, Danny A2 - T1 - Understanding user goals in web search T2 - Proceedings of the 13th international conference on World Wide Web PB - ACM CY - New York, NY, USA PY - 2004/ M2 - VL - IS - SP - 13 EP - 19 UR - http://doi.acm.org/10.1145/988672.988675 M3 - 10.1145/988672.988675 KW - goal KW - intention KW - mining KW - search KW - user KW - weblog L1 - SN - 1-58113-844-X N1 - Understanding user goals in web search N1 - AB - Previous work on understanding user web search behavior has focused on how people search and what they are searching for, but not why they are searching. In this paper, we describe a framework for understanding the underlying goals of user searches, and our experience in using the framework to manually classify queries from a web search engine. Our analysis suggests that so-called navigational" searches are less prevalent than generally believed while a previously unexplored "resource-seeking" goal may account for a large fraction of web searches. We also illustrate how this knowledge of user search goals might be used to improve future web search engines. ER - TY - JOUR AU - Iváncsy, Renáta AU - Vajk, István T1 - Frequent Pattern Mining in Web Log Data JO - Acta Polytechnica Hungarica PY - 2006/ VL - 3 IS - 1 SP - EP - UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.4559 M3 - KW - association KW - frequent KW - itemset KW - mining KW - pattern KW - rule KW - weblog L1 - SN - N1 - CiteSeerX — Frequent Pattern Mining in Web Log Data N1 - AB - Abstract: Frequent pattern mining is a heavily researched area in the field of data mining with wide range of applications. One of them is to use frequent pattern discovery methods in Web log data. Discovering hidden information from Web log data is called Web usage mining. The aim of discovering frequent patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. In this paper three pattern mining approaches are investigated from the Web usage mining point of view. The different patterns in Web log mining are page sets, page sequences and page graphs. ER - TY - JOUR AU - Suneetha, K. R. AU - Krishnamoorthy, K. R. T1 - Identifying User Behavior by Analyzing Web Server

Access Log File JO - International Journal of Computer Science and Network Security PY - 2005/ VL - 9 IS - 4 SP - 327 EP - 332 UR - M3 - KW - asociation KW - count KW - hit KW - mining KW - rules KW - weblog L1 - SN - N1 - N1 - AB - ER - TY - JOUR AU - Nicholas, David AU - Huntington, Paul AU - Jamali, Hamid R. T1 - Diversity in the Information Seeking Behaviour of the Virtual Scholar: Institutional Comparisons JO - The Journal of Academic Librarianship PY - 2007/ VL - 33 IS - 6 SP - 629 EP - 638 UR - http://www.sciencedirect.com/science/article/pii/S0099133307001759 M3 - 10.1016/j.acalib.2007.09.001 KW - Diversity KW - behaviour KW - mining KW - oscholar KW - seeking KW - weblog L1 - SN - N1 - ScienceDirect.com - The Journal of Academic Librarianship - Diversity in the Information Seeking Behaviour of the Virtual Scholar: Institutional Comparisons N1 - AB - The logs of four universities using the OhioLINK journal system were evaluated for a period of fifteen months using deep log analysis methods in order to compare and contrast the information seeking behaviour of their users. Large differences were found, especially between the research and teaching universities. Methodological problems associated with making the comparisons are discussed in some detail especially in terms of defining online sessions. ER - TY - JOUR AU - Huntington, Paul AU - Nicholas, David AU - Jamali, Hamid R. T1 - Website usage metrics: A re-assessment of session data JO - Information Processing & Management PY - 2008/ VL - 44 IS - 1 SP - 358 EP - 372 UR - http://www.sciencedirect.com/science/article/pii/S0306457307000817 M3 - 10.1016/j.ipm.2007.03.003 KW - mining KW - proxy KW - session KW - timeout KW - weblog L1 - SN - N1 - ScienceDirect.com - Information Processing & Management - Website usage metrics: A re-assessment of session data N1 - AB - Metrics derived from user visits or sessions provide a means of evaluating Websites and an important insight into online information seeking behaviour, the most important of them being the duration of sessions and the number of pages viewed in a session, a possible busyness indicator. However, the identification of session (termed often ‘sessionization’) is fraught with difficulty in that there is no way of determining from a transactional log file that a user has ended their session. No one logs out. Instead a session delimiter has to be applied and this is typically done on the basis of a standard period of inactivity. To date researchers have discussed the issue of a time out delimiter in terms of a single value and if a page view time exceeds the cut-off value the session is deemed to have ended. This approach assumes that page view time is a single distribution and that the cut-off value is one point on that distribution. The authors however argue that page time distribution is composed of a number of quite separate view time distributions because of the marked differences in view times between pages (abstract, contents page, full text). This implies that a number of timeout delimiters should be applied. Employing data from a study of the OhioLINK digital journal library, the authors demonstrate how the setting of a time out delimiter impacts on the estimate of page view time and the number of estimated session. Furthermore, they also show how a number of timeout delimiters might apply and they argue that this gives a better and more robust estimate of the number of sessions, session time and page view time compared to an application of a single timeout delimiter. ER - TY - JOUR AU - Park, Soyeon AU - Lee, Joon Ho AU - Bae, Hee Jin T1 - End user searching: A Web log analysis of NAVER, a Korean Web search engine JO - Library & Information Science Research PY - 2005/ VL - 27 IS - 2 SP - 203 EP - 221 UR - http://www.sciencedirect.com/science/article/pii/S0740818805000083 M3 - 10.1016/j.lisr.2005.01.013 KW - mining KW - query KW - search KW - term KW - weblog L1 - SN - N1 - ScienceDirect.com - Library & Information Science Research - End user searching: A Web log analysis of NAVER, a Korean Web search engine N1 - AB - Transaction logs of NAVER, a major Korean Web search engine, were analyzed to track the information-seeking behavior of Korean Web users. These transaction logs include more than 40 million queries collected over 1 week. This study examines current transaction log analysis methodologies and proposes a method for log cleaning, session definition, and query classification. A term definition method which is necessary for Korean transaction log analysis is also discussed. The results of this study show that users behave in a simple way: they type in short queries with a few query terms, seldom use advanced features, and view few results' pages. Users also behave in a passive way: they seldom change search environments set by the system. It is of interest that users tend to change their queries totally rather than adding or deleting terms to modify the previous queries. The results of this study might contribute to the development of more efficient and effective Web search engines and services. ER - TY - JOUR AU - Agosti, Maristella AU - Crivellari, Franco AU - Di Nunzio, GiorgioMaria T1 - Web log analysis: a review of a decade of studies about information acquisition, inspection and interpretation of user interaction JO - Data Mining and Knowledge Discovery PY - 2012/ VL - 24 IS - 3 SP - 663 EP - 696 UR - http://dx.doi.org/10.1007/s10618-011-0228-8 M3 - 10.1007/s10618-011-0228-8 KW - mining KW - review KW - weblog L1 - SN - N1 - Web log analysis: a review of a decade of studies about information acquisition, inspection and interpretation of user interaction - Springer N1 - AB - In the last decade, the importance of analyzing information management systems logs has grown, because log data constitute a relevant aspect in evaluating the quality of such systems. A review of 10 years of research on log analysis is presented in this paper. About 50 papers and posters from five major conferences and about 30 related journal papers have been selected to trace the history of the state-of-the-art in this field. The paper presents an overview of two main themes: Web search engine log analysis and Digital Library System log analysis. The problem of the analysis of different sources of log data and the distribution of data are investigated. ER - TY - CONF AU - Carman, Mark J. AU - Baillie, Mark AU - Gwadera, Robert AU - Crestani, Fabio A2 - T1 - A statistical comparison of tag and query logs T2 - Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval PB - ACM CY - New York, NY, USA PY - 2009/ M2 - VL - IS - SP - 123 EP - 130 UR - http://doi.acm.org/10.1145/1571941.1571965 M3 - 10.1145/1571941.1571965 KW - comparison KW - diagram KW - divergence KW - kullblack-Leibler KW - mining KW - tags KW - triangular KW - vocabulary KW - weblog L1 - SN - 978-1-60558-483-6 N1 - A statistical comparison of tag and query logs N1 - AB - We investigate tag and query logs to see if the terms people use to annotate websites are similar to the ones they use to query for them. Over a set of URLs, we compare the distribution of tags used to annotate each URL with the distribution of query terms for clicks on the same URL. Understanding the relationship between the distributions is important to determine how useful tag data may be for improving search results and conversely, query data for improving tag prediction. In our study, we compare both term frequency distributions using vocabulary overlap and relative entropy. We also test statistically whether the term counts come from the same underlying distribution. Our results indicate that the vocabulary used for tagging and searching for content are similar but not identical. We further investigate the content of the websites to see which of the two distributions (tag or query) is most similar to the content of the annotated/searched URL. Finally, we analyze the similarity for different categories of URLs in our sample to see if the similarity between distributions is dependent on the topic of the website or the popularity of the URL. ER - TY - JOUR AU - Strohmaier, Markus AU - Körner, Christian AU - Kern, Roman T1 - Understanding why users tag: A survey of tagging motivation literature and results from an empirical study JO - Web Semantics: Science, Services and Agents on the World Wide Web PY - 2012/ VL - 17 IS - 0 SP - 1 EP - 11 UR - http://www.sciencedirect.com/science/article/pii/S1570826812000820 M3 - 10.1016/j.websem.2012.09.003 KW - mining KW - motivation KW - survey KW - tagging KW - understanding KW - users KW - weblog L1 - SN - N1 - ScienceDirect.com - Web Semantics: Science, Services and Agents on the World Wide Web - Understanding why users tag: A survey of tagging motivation literature and results from an empirical study N1 - AB - While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems. ER - TY - JOUR AU - Jiang, Jing AU - Wilson, Christo AU - Wang, Xiao AU - Sha, Wenpeng AU - Huang, Peng AU - Dai, Yafei AU - Zhao, Ben Y. T1 - Understanding Latent Interactions in Online Social Networks JO - ACM Trans. Web PY - 2013/november VL - 7 IS - 4 SP - 18:1 EP - 18:39 UR - http://doi.acm.org/10.1145/2517040 M3 - 10.1145/2517040 KW - analyis KW - interaction KW - latent KW - log KW - mining KW - network KW - social KW - user KW - web L1 - SN - N1 - Understanding latent interactions in online social networks N1 - AB - Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs. ER - TY - CHAP AU - Agrawal, Rakesh AU - Christoforaki, Maria AU - Gollapudi, Sreenivas AU - Kannan, Anitha AU - Kenthapadi, Krishnaram AU - Swaminathan, Adith A2 - Glodeanu, CynthiaVera A2 - Kaytoue, Mehdi A2 - Sacarea, Christian T1 - Mining Videos from the Web for Electronic Textbooks T2 - Formal Concept Analysis PB - Springer International Publishing CY - PY - 2014/ VL - 8478 IS - SP - 219 EP - 234 UR - http://dx.doi.org/10.1007/978-3-319-07248-7_16 M3 - 10.1007/978-3-319-07248-7_16 KW - application KW - fca KW - mining KW - video L1 - SN - 978-3-319-07247-0 N1 - Mining Videos from the Web for Electronic Textbooks - Springer N1 - AB - We propose a system for mining videos from the web for supplementing the content of electronic textbooks in order to enhance their utility. Textbooks are generally organized into sections such that each section explains very few concepts and every concept is primarily explained in one section. Building upon these principles from the education literature and drawing upon the theory of ER -