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%0 Report
%A Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas & Strohmaier, Markus
%D 2014
%T Of course we share! Testing Assumptions about Social Tagging Systems
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%2 Of course we share! Testing Assumptions about Social Tagging Systems
%3 techreport
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%F doerfel2014course
%K 2014, analysis, assumptions, bibsonomy, data, folksonomy, log, myown, share, social, tagging, testing, weblog
%X Social tagging systems have established themselves as an important part in
today's web and have attracted the interest from our research community in a
variety of investigations. The overall vision of our community is that simply
through interactions with the system, i.e., through tagging and sharing of
resources, users would contribute to building useful semantic structures as
well as resource indexes using uncontrolled vocabulary not only due to the
easy-to-use mechanics. Henceforth, a variety of assumptions about social
tagging systems have emerged, yet testing them has been difficult due to the
absence of suitable data. In this work we thoroughly investigate three
available assumptions - e.g., is a tagging system really social? - by examining
live log data gathered from the real-world public social tagging system
BibSonomy. Our empirical results indicate that while some of these assumptions
hold to a certain extent, other assumptions need to be reflected and viewed in
a very critical light. Our observations have implications for the design of
future search and other algorithms to better reflect the actual user behavior.
%Z cite arxiv:1401.0629
%U http://arxiv.org/abs/1401.0629
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%0 Conference Proceedings
%A Doerfel, Stephan; Jäschke, Robert; Hotho, Andreas & Stumme, Gerd
%D 2012
%T Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation
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%B Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
%C New York, NY, USA
%I ACM
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%P 9--16
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%S RSWeb '12
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%@ 978-1-4503-1638-5
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%2 Leveraging publication metadata and social data into FolkRank for scientific publication recommendation
%3 inproceedings
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%F doerfel2012leveraging
%K 2012, data, info20pub, itegpub, itemRecommendation, leveraging, metadata, myown, publication, reco, recsys, social
%X The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.
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%U http://doi.acm.org/10.1145/2365934.2365937
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%0 Conference Proceedings
%A Hees, Jörn; Roth-Berghofer, Thomas & Dengel, Andreas
%D 2010
%T Linked Data Games: Simulating Human Association with Linked Data
%E Atzmüller, Martin; Benz, Dominik; Hotho, Andreas & Stumme, Gerd
%B Proceedings of LWA2010 - Workshop-Woche: Lernen, Wissen {\&} Adaptivitaet
%C Kassel, Germany
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%1 http://www.kde.cs.uni-kassel.de/conf/lwa10/papers/wm2.pdf
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%3 inproceedings
%4 lwa2010
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%F hees2010linked
%K data, games, linked, simulating
%X Teaching machines to understand human communication is one of the central goals of artificial intelligence. Psychological research indicates that human associations are an essential requirement to understand human communication. In this paper the hypothesis is presented, that simulating human associations with the help of Linked Data could greatly improve text understanding capabilities of machines. To more thoroughly investigate whether human associations can be simulated with Linked Data, two preliminary problems are identified: (i) There does not seem to be a reasonable ground truth for human associations and (ii) while human associations have different strengths, Linked Data treats all triples equally and does not provide edge weights. Two ideas for games in accordance with Luis von Ahn's Games with a Purpose are proposed, turning the tedious process of entering associations or ratings into fun games.
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%0 Conference Proceedings
%A Benevenuto, Fabr\'\i,cio; Rodrigues, Tiago; Cha, Meeyoung & Almeida, Virg\'\i,lio
%D 2009
%T Characterizing User Behavior in Online Social Networks
%E 
%B Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference
%C New York, NY, USA
%I ACM
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%P 49--62
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%S IMC '09
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%2 Characterizing user behavior in online social networks
%3 inproceedings
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%F benevenuto2009characterizing
%K analysis, behavior, clickstream, data, log, network, sna, social, user
%X Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.
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%U http://doi.acm.org/10.1145/1644893.1644900
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%0 Journal Article
%A Go, A; Huang, L & Bhayani, R
%D 2009
%T Sentiment Analysis of Twitter Data
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%B Entropy
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%I Association for Computational Linguistics
%V 2009
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%F Go_Huang_Bhayani_2009
%K analysis, data, evaluation, sentiment, twitter
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%U http://nlp.stanford.edu/courses/cs224n/2009/fp/3.pdf
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%0 Conference Proceedings
%A Wang, Haixun; 0010, Wei Wang; Yang, Jiong & Yu, Philip S.
%D 2002
%T Clustering by pattern similarity in large data sets.
%E Franklin, Michael J.; Moon, Bongki & Ailamaki, Anastassia
%B SIGMOD Conference
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%I ACM
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%P 394-405
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%3 inproceedings
%4 conf/sigmod/2002
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%F conf/sigmod/WangWYY02
%K algorithm, clustering, data, fca?, large, pClusters, paper, pattern
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%U http://dblp.uni-trier.de/db/conf/sigmod/sigmod2002.html#WangWYY02
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