A Memory Network Information Retrieval Model for Identification of News Misinformation

The speed and volume at which misinformation spreads on social media have motivated efforts to automate fact-checking which begins with stance detection. For fake news stance detection, for example, many classification-based models have been proposed often with high complexity and hand-crafted features. Although these models can achieve high accuracy scores on a targeted small corpus of fake news, few are evaluated on a larger corpus of fake and conspiracy sites due to efficiency limitations and the lack of compatibility with the actual fact-checking process. In this article, we propose a practical two-stage stance detection model that is tailored to the real-life problem. Specifically, we integrate an information retrieval system with an end to end memory network model to sort articles based on their relevance to the claim and then identify the fine-grained stance of each relevant article towards its given claim. We evaluate our model on the Fake News Challenge dataset (FNC-1). The results show that the performance of our model is comparable to those of the state-of-the-art models, average weighted accuracy of 82.1, while it closely follows the real-life process of fact-checking. We also validate our model with a large dataset from a real-life fact-checking website (i.e., Snopes.com ), and the findings demonstrate the capability of the model in distinguishing false from true news headlines.