TweetCred: Real-Time Credibility Assessment of Content on Twitter

TL;DR

This paper introduces TweetCred, a real-time, semi-supervised credibility scoring system for tweets, evaluated on over five million tweets and used by over a thousand users, addressing the challenge of misinformation during crises.

Contribution

It presents the first real-time credibility assessment system for Twitter, combining semi-supervised ranking models with large-scale user evaluation.

Findings

Credibility scores computed for 5.4 million tweets

System demonstrated effective response time and usability

First large-scale real-time credibility system for Twitter

Abstract

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.