Learning to Detect Misleading Content on Twitter

The publication and spread of misleading content is a problem of increasing magnitude, complexity and consequences in a world that is increasingly relying on user-generated content for news sourcing. To this end, multimedia analysis techniques could serve as an assisting tool to spot and debunk misleading content on the Web. In this paper, we tackle the problem of misleading multimedia content detection on Twitter in real time. We propose a number of new features and a new semi-supervised learning event adaptation approach that helps generalize the detection capabilities of trained models to unseen content, even when the event of interest is of different nature compared to that used for training. Combined with bagging, the proposed approach manages to outperform previous systems by a significant margin in terms of accuracy. Moreover, in order to communicate the verification process to end users, we develop a web-based application for visualizing the results.