TY - JOUR AU - Atzmueller, Martin T1 - Mining Social Media: Key Players, Sentiments, and Communities JO - WIREs: Data Mining and Knowledge Discovery PY - 2012/ VL - In Press IS - SP - EP - UR - DO - KW - 2012 KW - analysis KW - community KW - data KW - detection KW - itegpub KW - mining KW - network KW - opinion KW - sentiment KW - social KW - venus KW - vikamine L1 - SN - N1 - N1 - AB - ER - TY - CONF AU - Bullock, Beate Navarro AU - Lerch, Hana AU - Alexander, Ro AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - Privacy-aware spam detection in social bookmarking systems T2 - Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies PB - ACM C1 - New York, NY, USA PY - 2011/ CY - VL - IS - SP - 15:1 EP - 15:8 UR - http://doi.acm.org/10.1145/2024288.2024306 DO - 10.1145/2024288.2024306 KW - 2011 KW - aware KW - classification KW - data-mining KW - detection KW - info20 KW - itegpub KW - myown KW - social KW - spam KW - spam-detection KW - web2.0 KW - web20 L1 - SN - 978-1-4503-0732-1 N1 - Privacy-aware spam detection in social bookmarking systems N1 - AB - 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. ER - TY - CONF AU - Krause, Beate AU - Schmitz, Christoph AU - Hotho, Andreas AU - Stumme, Gerd A2 - T1 - The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems T2 - AIRWeb '08: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web PB - ACM C1 - New York, NY, USA PY - 2008/04 CY - VL - IS - SP - 61 EP - 68 UR - http://airweb.cse.lehigh.edu/2008/submissions/krause_2008_anti_social_tagger.pdf DO - 10.1145/1451983.1451998 KW - 2008 KW - bookmarking KW - detection KW - itegpub KW - l3s KW - myown KW - seminar KW - spam KW - summer L1 - SN - 978-1-60558-159-0 N1 - N1 - AB - 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. ER - TY - CONF AU - Falkowski, Tanja AU - Barth, Anja AU - Spiliopoulou, Myra A2 - T1 - DENGRAPH: A Density-based Community Detection Algorithm T2 - In Proc. of the 2007 IEEE / WIC / ACM International Conference on Web Intelligence, PB - C1 - PY - 2007/ CY - VL - IS - SP - 112 EP - 115 UR - http://wwwiti.cs.uni-magdeburg.de/~tfalkows/publ/2007/WI_FalBarSpi07.pdf DO - KW - algorithm KW - based KW - clustering KW - community KW - density KW - detection L1 - SN - N1 - Tanja Falkowski N1 - AB - ER - TY - CONF AU - Hotho, Andreas AU - Jäschke, Robert AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Avrithis, Yannis S. A2 - Kompatsiaris, Yiannis A2 - Staab, Steffen A2 - O'Connor, Noel E. T1 - Trend Detection in Folksonomies T2 - Proc. First International Conference on Semantics And Digital Media Technology (SAMT) PB - Springer C1 - Heidelberg PY - 2006/12 CY - VL - 4306 IS - SP - 56 EP - 70 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006trend.pdf DO - KW - 2006 KW - UniK KW - detection KW - folkrank KW - folksonomy KW - hotho KW - intranet KW - itegpub KW - jaeschke KW - l3s KW - myown KW - nepomuk KW - pagerank KW - schmitz KW - stumme KW - tagorapub KW - trend KW - triadic L1 - SN - 3-540-49335-2 N1 - N1 - AB - 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. ER - TY - CONF AU - Jäschke, Robert AU - Hotho, Andreas AU - Schmitz, Christoph AU - Stumme, Gerd A2 - Braß, Stefan A2 - Hinneburg, Alexander T1 - Wege zur Entdeckung von Communities in Folksonomies T2 - Proc. 18. Workshop Grundlagen von Datenbanken PB - Martin-Luther-Universität C1 - Halle-Wittenberg PY - 2006/06 CY - VL - IS - SP - 80 EP - 84 UR - http://www.kde.cs.uni-kassel.de/stumme/papers/2006/jaeschke2006wege.pdf DO - KW - 2006 KW - bibsonomy KW - communities KW - community KW - detection KW - itegpub KW - l3s KW - myown KW - nepomuk KW - tagging L1 - SN - N1 - N1 - AB - 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. ER - TY - JOUR AU - Kumar, Ravi AU - Raghavan, Prabhakar AU - Rajagopalan, Sridhar AU - Tomkins, Andrew T1 - Trawling the Web for emerging cyber-communities JO - Computer Networks PY - 1999/ VL - 31 IS - 11--16 SP - 1481 EP - 1493 UR - citeseer.ist.psu.edu/kumar99trawling.html DO - KW - communities KW - community KW - detection KW - web L1 - SN - N1 - N1 - AB - ER -