A Deep Transfer Learning Approach for Fake News Detection

Fake or incorrect or miss-information detection has nowadays attracted attention to the researchers and developers because of the huge information overloaded in the web. This problem can be considered as equivalent to lie detection, truthfulness identification or stance detection. In our particular work, we focus on deciding whether the title of a news is consistent with its body text- a problem equivalent to fake information identification. In this paper, we propose a deep transfer learning approach where the problem of detecting title-body consistency is posed from the viewpoint of Textual Entailment (TE) where the title is considered as a hypothesis and news body is treated as a premise. The idea is to decide whether the body infers the title or not. Evaluation on the existing benchmark datasets, namely Fake News Challenge (FNC) dataset (released in Fake News Challenge Stage 1 (FNC-I): Stance Detection) show the efficacy of our proposed approach in comparison to the state-of-the-art systems.