  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.
