%0 
%0 Journal Article
%A Atzmueller, Martin
%D 2012
%T Mining Social Media: Key Players, Sentiments, and Communities
%E 
%B WIREs: Data Mining and Knowledge Discovery
%C 
%I 
%V In Press
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%F Atzmueller:12c
%K 2012, analysis, community, data, detection, itegpub, mining, network, opinion, sentiment, social, venus, vikamine
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%0 
%0 Conference Proceedings
%A Bullock, Beate Navarro; Lerch, Hana; Ro\ssnagel, Alexander; Hotho, Andreas & Stumme, Gerd
%D 2011
%T Privacy-aware spam detection in social bookmarking systems
%E 
%B Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 15:1--15:8
%& 
%Y 
%S i-KNOW '11
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%@ 978-1-4503-0732-1
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%2 Privacy-aware spam detection in social bookmarking systems
%3 inproceedings
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%F bullock2011privacyaware
%K 2011, aware, classification, data-mining, detection, info20, itegpub, myown, social, spam, spam-detection, web2.0, web20
%X With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.
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%U http://doi.acm.org/10.1145/2024288.2024306
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%0 Conference Proceedings
%A Krause, Beate; Schmitz, Christoph; Hotho, Andreas & Stumme, Gerd
%D 2008
%T The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems
%E 
%B AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web
%C New York, NY, USA
%I ACM
%V 
%6
%N 
%P 61--68
%& 
%Y 
%S 
%7
%8 April
%9
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%! 
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%@ 978-1-60558-159-0
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%F krause2008antisocial
%K 2008, bookmarking, detection, itegpub, l3s, myown, seminar, spam, summer
%X The annotation of web sites in social bookmarking systemshas become a popular way to manage and find informationon the web. The community structure of such systems attractsspammers: recent post pages, popular pages or specifictag pages can be manipulated easily. As a result, searchingor tracking recent posts does not deliver quality resultsannotated in the community, but rather unsolicited, oftencommercial, web sites. To retain the benefits of sharingone’s web content, spam-fighting mechanisms that can facethe flexible strategies of spammers need to be developed.
%Z 
%U http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf
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%^

%0 
%0 Conference Proceedings
%A Falkowski, Tanja; Barth, Anja & Spiliopoulou, Myra
%D 2007
%T DENGRAPH: A Density-based Community Detection Algorithm
%E 
%B In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,
%C 
%I 
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%6
%N 
%P 112-115
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%2 Tanja Falkowski
%3 inproceedings
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%F FalBarSpi07
%K algorithm, based, clustering, community, density, detection
%X 
%Z 
%U http://wwwiti.cs.uni-magdeburg.de/~tfalkows/publ/2007/WI_FalBarSpi07.pdf
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%0 
%0 Conference Proceedings
%A Hotho, Andreas; Jäschke, Robert; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T Trend Detection in Folksonomies
%E Avrithis, Yannis S.; Kompatsiaris, Yiannis; Staab, Steffen & O'Connor, Noel E.
%B Proc. First International Conference on Semantics And Digital Media Technology (SAMT) 
%C Heidelberg
%I Springer
%V 4306
%6
%N 
%P 56-70
%& 
%Y 
%S LNCS
%7
%8 December
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%? 
%! 
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%@ 3-540-49335-2
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%F hotho2006trend
%K 2006, UniK, detection, folkrank, folksonomy, hotho, intranet, itegpub, jaeschke, l3s, myown, nepomuk, pagerank, schmitz, stumme, tagorapub, trend, triadic
%X As the number of resources on the web exceeds by far the number of
documents one can track, it becomes increasingly difficult to remain
up to date on ones own areas of interest. The problem becomes more
severe with the increasing fraction of multimedia data, from which
it is difficult to extract some conceptual description of their
contents.

One way to overcome this problem are social bookmark tools, which
are rapidly emerging on the web. In such systems, users are setting
up lightweight conceptual structures called folksonomies, and
overcome thus the knowledge acquisition bottleneck. As more and more
people participate in the effort, the use of a common vocabulary
becomes more and more stable. We present an approach for discovering
topic-specific trends within folksonomies. It is based on a
differential adaptation of the PageRank algorithm to the triadic
hypergraph structure of a folksonomy. The approach allows for any
kind of data, as it does not rely on the internal structure of the
documents. In particular, this allows to consider different data
types in the same analysis step. We run experiments on a large-scale
real-world snapshot of a social bookmarking system.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf
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%0 
%0 Conference Proceedings
%A Jäschke, Robert; Hotho, Andreas; Schmitz, Christoph & Stumme, Gerd
%D 2006
%T Wege zur Entdeckung von Communities in Folksonomies
%E Braß, Stefan & Hinneburg, Alexander
%B Proc. 18. Workshop Grundlagen von Datenbanken
%C Halle-Wittenberg
%I Martin-Luther-Universität 
%V 
%6
%N 
%P 80-84
%& 
%Y 
%S 
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%8 June
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%F jaeschke2006wege
%K 2006, bibsonomy, communities, community, detection, itegpub, l3s, myown, nepomuk, tagging
%X Ein wichtiger Baustein des neu entdeckten World Wide Web -- des "`Web 2.0"'  -- stellen
Folksonomies dar. In diesen Systemen können Benutzer gemeinsam Ressourcen verwalten und
mit Schlagwörtern versehen. Die dadurch entstehenden begrifflichen Strukturen stellen
ein interessantes Forschungsfeld dar. Dieser Artikel untersucht Ansätze und Wege zur 
Entdeckung und Strukturierung von Nutzergruppen ("Communities") in Folksonomies.
%Z 
%U http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006wege.pdf
%+
%^

%0 
%0 Journal Article
%A Kumar, Ravi; Raghavan, Prabhakar; Rajagopalan, Sridhar & Tomkins, Andrew
%D 1999
%T Trawling the Web for emerging cyber-communities
%E 
%B Computer Networks 
%C 
%I 
%V 31
%6
%N 11--16
%P 1481--1493
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%F kumar99trawling
%K communities, community, detection, web
%X 
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%U citeseer.ist.psu.edu/kumar99trawling.html
%+
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