CbI: Improving Credibility of User-Generated Content on Facebook

Online Social Networks (OSNs) have become a popular platform to share information with each other. Fake news often spread rapidly in OSNs especially during news-making events, e.g. Earthquake in Chile (2010) and Hurricane Sandy in the USA (2012). A potential solution is to use machine learning techniques to assess the credibility of a post automatically, i.e. whether a person would consider the post believable or trustworthy. In this paper, we provide a fine-grained definition of credibility. We call a post to be credible if it is accurate, clear, and timely. Hence, we propose a system which calculates the Accuracy, Clarity, and Timeliness (A-C-T) of a Facebook post which in turn are used to rank the post for its credibility. We experiment with 1,056 posts created by 107 pages that claim to belong to news-category. We use a set of 152 features to train classification models each for A-C-T using supervised algorithms. We use the best performing features and models to develop a RESTful API and a Chrome browser extension to rank posts for its credibility in real-time. The random forest algorithm performed the best and achieved ROC AUC of 0.916, 0.875, and 0.851 for A-C-T respectively.