We propose an approach that predicts whether a tweet, which is accompanied by multimedia content (image/video), is trustworthy or deceptive. We test different combinations of quality and trust-oriented features (tweet-based, userbased and forensics) in tandem with a standard classification and an agreement-retraining technique, with the goal of predicting the most likely label (fake or real) fo...
