KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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User Reputation computation Method Based on Implicit Ratings on Social Media

  • Bok, Kyoungsoo (School of Information and Communication Engineering, Chungbuk National University) ;
  • Yun, Jinkyung (Bigdata Team, Exem, Inc.) ;
  • Kim, Yeonwoo (School of Information and Communication Engineering, Chungbuk National University) ;
  • Lim, Jongtae (School of Information and Communication Engineering, Chungbuk National University) ;
  • Yoo, Jaesoo (School of Information and Communication Engineering, Chungbuk National University)
  • Received : 2016.07.28
  • Accepted : 2017.01.13
  • Published : 2017.03.31
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Abstract

Social network services have recently changed from environments for simply building connections among users to open platforms for generating and sharing various forms of information. Existing user reputation computation methods are inadequate for determining the trust in users on social media where explicit ratings are rare, because they determine the trust in users based on user profile, explicit relations, and explicit ratings. To solve this limitation of previous research, we propose a user reputation computation method suitable for the social media environment by incorporating implicit as well as explicit ratings. Reliable user reputation is estimated by identifying malicious information raters, modifying explicit ratings, and applying them to user reputation scores. The proposed method incorporates implicit ratings into user reputation estimation by differentiating positive and negative implicit ratings. Moreover, the method generates user reputation scores for individual categories to determine a given user's expertise, and incorporates the number of users who participated in rating to determine a given user's influence. This allows reputation scores to be generated also for users who have received no explicit ratings, and, thereby, is more suitable for social media. In addition, based on the user reputation scores, malicious information providers can be identified.

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