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Content Mining of Microblogs

  • M. Özgür CingizEmail author
  • Banu Diri
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

Emergence of Web 2.0, internet users can share their contents with other users using social networks. In this chapter microbloggers’ contents are evaluated with respect to how they reflect their categories. Migrobloggers’ category information, which is one of the four categories that are economy sport, entertainment or technology, is taken from wefollow.com application. 3337 RSS news feeds, whose category labels are same with microbloggers’ contributions, are used as training data for classification. Unlike the similar studies if a feature of microblog doesn’t appear in RSS news feeds as a feature, this feature is omitted so abbreviations and nonsense words in microblogs can be eliminated. In this study two types of users’ contributions are taken as test data. These users are normal microbloggers and bots. Classification results show that bots provide more categorical content than normal users.

Keywords

Support Vector Machine Feature Selection Method Decision Boundary Normal User Text Document 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Engineering DepartmentYıldız Teknik ÜniversitesiIstanbulTurkey

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