Mining Social Media: Key Players, Sentiments, and Communities.
WIREs: Data Mining and Knowledge Discovery, In Press, 2012.
Martin Atzmueller.
[BibTeX]
Privacy-aware spam detection in social bookmarking systems.
In:
Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, Reihe i-KNOW '11, Seiten 15:1-15:8.
ACM, New York, NY, USA, 2011.
Beate Navarro Bullock, Hana Lerch, Alexander Ro Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems.
In:
AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web, Seiten 61-68.
ACM, New York, NY, USA, 2008.
Beate Krause, Christoph Schmitz, Andreas Hotho und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
DENGRAPH: A Density-based Community Detection Algorithm.
In:
In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence,, Seiten 112-115.
2007.
Tanja Falkowski, Anja Barth und Myra Spiliopoulou.
[doi]
[BibTeX]
Trend Detection in Folksonomies.
In: Y. S. Avrithis, Y. Kompatsiaris, S. Staab und N. E. O'Connor
(Herausgeber):
Proc. First International Conference on Semantics And Digital Media Technology (SAMT) , Band 4306, Reihe LNCS, Seiten 56-70.
Springer, Heidelberg, 2006.
Andreas Hotho, Robert Jäschke, Christoph Schmitz und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
Wege zur Entdeckung von Communities in Folksonomies.
In: S. Braß und A. Hinneburg
(Herausgeber):
Proc. 18. Workshop Grundlagen von Datenbanken, Seiten 80-84.
Martin-Luther-Universität , Halle-Wittenberg, 2006.
Robert Jäschke, Andreas Hotho, Christoph Schmitz und Gerd Stumme.
[doi]
[Kurzfassung]
[BibTeX]
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.
Trawling the Web for emerging cyber-communities.
Computer Networks , 31(11--16):1481-1493, 1999.
Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan und Andrew Tomkins.
[doi]
[BibTeX]