• DocumentCode
    588958
  • Title

    Detecting Spam in Chinese Microblogs - A Study on Sina Weibo

  • Author

    Lin Liu ; Kun Jia

  • Author_Institution
    Sch. of Comput. Sci., Beijing Univ. of Posts & Telecommun., Beijing, China
  • fYear
    2012
  • fDate
    17-18 Nov. 2012
  • Firstpage
    578
  • Lastpage
    581
  • Abstract
    Sina Weibo is the most popular and fast growing microblogging social network in China. However, more and more spam messages are also emerging on Sina Weibo. How to detect these spam is essential for the social network security. While most previous studies attempt to detect the microblogging spam by identifying spammers, in this paper, we want to exam whether we can detect the spam by each single Weibo message, because we notice that more and more spam Weibos are posted by normal users or even popular verified users. We propose a Weibo spam detection method based on machine learning algorithm. In addition, different from most existing microblogging spam detection methods which are based on English microblogs, our method is designed to deal with the features of Chinese microblogs. Our extensive empirical study shows the effectiveness of our approach.
  • Keywords
    computer network security; learning (artificial intelligence); social networking (online); unsolicited e-mail; Chinese microblog; Sina Weibo; Weibo spam detection method; machine learning algorithm; microblogging social network; microblogging spam detection; social network security; spam message detection; spammer identification; Feature extraction; Logistics; Machine learning; Machine learning algorithms; Support vector machines; Twitter; Vectors; data mining; machine learning; spam detection; web security;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Security (CIS), 2012 Eighth International Conference on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-1-4673-4725-9
  • Type

    conf

  • DOI
    10.1109/CIS.2012.135
  • Filename
    6406086