Lorince, J.; Joseph, K. & Todd, P. (2015), Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids?, in Nitin Agarwal; Kevin Xu & Nathaniel Osgood, ed., 'Social Computing, Behavioral-Cultural Modeling, and Prediction' , Springer International Publishing, , pp. 141-152 .
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In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do

Zoller, D.; Doerfel, S.; Jäschke, R.; Stumme, G. & Hotho, A. (2015), On Publication Usage in a Social Bookmarking System, in 'Proceedings of the 2015 ACM Conference on Web Science' .
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Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.

2014

Duarte Torres, S.; Weber, I. & Hiemstra, D. (2014), 'Analysis of Search and Browsing Behavior of Young Users on the Web', ACM Transactions on the Web 8 (2) , 7:1--7:54 .
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The Internet is increasingly used by young children for all kinds of purposes. Nonetheless, there are not many resources especially designed for children on the Internet and most of the content online is designed for grown-up users. This situation is problematic if we consider the large differences between young users and adults since their topic interests, computer skills, and language capabilities evolve rapidly during childhood. There is little research aimed at exploring and measuring the difficulties that children encounter on the Internet when searching for information and browsing for content. In the first part of this work, we employed query logs from a commercial search engine to quantify the difficulties children of different ages encounter on the Internet and to characterize the topics that they search for. We employed query metrics (e.g., the fraction of queries posed in natural language), session metrics (e.g., the fraction of abandoned sessions), and click activity (e.g., the fraction of ad clicks). The search logs were also used to retrace stages of child development. Concretely, we looked for changes in interests (e.g., the distribution of topics searched) and language development (e.g., the readability of the content accessed and the vocabulary size). In the second part of this work, we employed toolbar logs from a commercial search engine to characterize the browsing behavior of young users, particularly to understand the activities on the Internet that trigger search. We quantified the proportion of browsing and search activity in the toolbar sessions and we estimated the likelihood of a user to carry out search on the Web vertical and multimedia verticals (i.e., videos and images) given that the previous event is another search event or a browsing event. We observed that these metrics clearly demonstrate an increased level of confusion and unsuccessful search sessions among children. We also found a clear relation between the reading level of the clicked pages and characteristics of the users such as age and educational attainment. In terms of browsing behavior, children were found to start their activities on the Internet with a search engine (instead of directly browsing content) more often than adults. We also observed a significantly larger amount of browsing activity for the case of teenager users. Interestingly we also found that if children visit knowledge-related Web sites (i.e., information-dense pages such as Wikipedia articles), they subsequently do more Web searches than adults. Additionally, children and especially teenagers were found to have a greater tendency to engage in multimedia search, which calls to improve the aggregation of multimedia results into the current search result pages.

2011

Haustein, S. & Siebenlist, T. (2011), 'Applying social bookmarking data to evaluate journal usage', Journal of Informetrics 5 (3) , 446 - 457 .
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Web 2.0 technologies are finding their way into academics: specialized social bookmarking services allow researchers to store and share scientific literature online. By bookmarking and tagging articles, academic prosumers generate new information about resources, i.e. usage statistics and content description of scientific journals. Given the lack of global download statistics, the authors propose the application of social bookmarking data to journal evaluation. For a set of 45 physics journals all 13,608 bookmarks from CiteULike, Connotea and BibSonomy to documents published between 2004 and 2008 were analyzed. This article explores bookmarking data in STM and examines in how far it can be used to describe the perception of periodicals by the readership. Four basic indicators are defined, which analyze different aspects of usage: Usage Ratio, Usage Diffusion, Article Usage Intensity and Journal Usage Intensity. Tags are analyzed to describe a reader-specific view on journal content.

2009

Schneider, F.; Feldmann, A.; Krishnamurthy, B. & Willinger, W. (2009), Understanding Online Social Network Usage from a Network Perspective, in 'Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference' , ACM, New York, NY, USA , pp. 35--48 .
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Online Social Networks (OSNs) have already attracted more than half a billion users. However, our understanding of which OSN features attract and keep the attention of these users is poor. Studies thus far have relied on surveys or interviews of OSN users or focused on static properties, e. g., the friendship graph, gathered via sampled crawls. In this paper, we study how users actually interact with OSNs by extracting clickstreams from passively monitored network traffic. Our characterization of user interactions within the OSN for four different OSNs (Facebook, LinkedIn, Hi5, and StudiVZ) focuses on feature popularity, session characteristics, and the dynamics within OSN sessions. We find, for example, that users commonly spend more than half an hour interacting with the OSNs while the byte contributions per OSN session are relatively small.

2008

Glushko, R. J.; Maglio, P. P.; Matlock, T. & Barsalou, L. W. (2008), 'Categorization in the wild ', Trends in Cognitive Sciences 12 (4) , 129 - 135 .
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In studying categorization, cognitive science has focused primarily on cultural categorization, ignoring individual and institutional categorization. Because recent technological developments have made individual and institutional classification systems much more available and powerful, our understanding of the cognitive and social mechanisms that produce these systems is increasingly important. Furthermore, key aspects of categorization that have received little previous attention emerge from considering diverse types of categorization together, such as the social factors that create stability in classification systems, and the interoperability that shared conceptual systems establish between agents. Finally, the profound impact of recent technological developments on classification systems indicates that basic categorization mechanisms are highly adaptive, producing new classification systems as the situations in which they operate change.

2006

Golder, S. A. & Huberman, B. A. (2006), 'Usage patterns of collaborative tagging systems', Journal of Information Science 32 (2) , 198-208 .
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Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge.

Golder, S. A. & Huberman, B. A. (2006), 'Usage patterns of collaborative tagging systems', Journal of Information Science 32 (2) , 198--208 .
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Golder, S. A. & Huberman, B. A. (2006), 'Usage patterns of collaborative tagging systems', Journal of Information Science 32 (2) , 198-208 .
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Millen, D. R. & Feinberg, J. (2006), Using Social Tagging to Improve Social Navigation, in 'Workshop on the Social Navigation and Community based Adaptation Technologies' .
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2005

Nicholas, D.; Huntington, P. & Watkinson, A. (2005), 'Scholarly journal usage: the results of deep log analysis', Journal of Documentation 61 (2) , 248--280 .
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2004

Berendt, B.; Hotho, A. & Stumme, G. (2004), Usage Mining for and on the Semantic Web, in Hillol Kargupta; Anupam Joshi; Krishnamoorthy Sivakumar & Yelena Yesha, ed., 'Data Mining Next Generation Challenges and Future Directions' , AAAI Press, Boston , pp. 461-481 .
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Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of data Web mining operates on, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web resources and navigation behavior are increasingly being used. This fits exactly with the aims of the Semantic Web: the Semantic Web enriches the WWW by machine-processable information which supports the user in his tasks. In this paper, we discuss the interplay of the Semantic Web with Web Mining, with a specific focus on usage mining.

2002

Stumme, G.; Berendt, B. & Hotho, A. (2002), Usage Mining for and on the Semantic Web, in 'Proc. NSF Workshop on Next Generation Data Mining' , pp. 77-86 .
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Zhang, D. & Dong, Y. (2002), 'A novel Web usage mining approach for search engines', Computer Networks 39 (3) , 303--310 .
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1998

Abrams, D.; Baecker, R. & Chignell, M. (1998), Information archiving with bookmarks: personal Web space construction and organization, in 'Proceedings of the SIGCHI Conference on Human Factors in Computing Systems' , ACM Press/Addison-Wesley Publishing Co., New York, NY, USA , pp. 41--48 .
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Jones, S.; Cunningham, S. J. & McNab, R. (1998), Usage analysis of a digital library, in 'Proceedings of the third ACM conference on Digital libraries' , ACM, New York, NY, USA , pp. 293--294 .
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