International Conference on Social Informatics

SocInfo 2014: Social Informatics pp 228-243 | Cite as

TweetCred: Real-Time Credibility Assessment of Content on Twitter

  • Aditi Gupta
  • Ponnurangam Kumaraguru
  • Carlos Castillo
  • Patrick Meier
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8851)


During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or “tweets”). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting.

In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in TweetCred, a real-time system that assigns a credibility score to tweets in a user’s timeline. TweetCred, available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us to evaluate TweetCred in terms of response time, effectiveness and usability. To the best of our knowledge, this is the first research work to develop a real-time system for credibility on Twitter, and to evaluate it on a user base of this size.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Aditi Gupta
    • 1
  • Ponnurangam Kumaraguru
    • 1
  • Carlos Castillo
    • 2
  • Patrick Meier
    • 2
  1. 1.Indraprastha Institute of Information TechnologyDelhiIndia
  2. 2.Qatar Computing Research InstituteDohaQatar

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