Extracting attributed verification and debunking reports from social media: MediaEval-2015 trust and credibility analysis of image and video

Journalists are increasingly turning to technology for pre-filtering and automation of the simpler parts of the verification process. We present results from our semi-automated approach to trust and credibility analysis of tweets referencing suspicious images and videos. We use natural language processing to extract evidence from tweets in the form of fake & genuine claims attributed to trusted and untrusted sources. Results for team UoS-ITI in theMediaEval 2015 Verifying Multimedia Use task are reported. Our 'fake' tweet classifier precision scores range from 0.94 to 1.0 (recall 0.43 to 0.72), and our 'real' tweet classifier precision scores range from 0.74 to 0.78 (recall 0.51 to 0.74). Image classification precision scores range from 0.62 to 1.0 (recall 0.04 to 0.23). Our approach can automatically alert journalists in real-time to trustworthy claims verifying or debunking viral images or video