LEARNING SPARSE ALIGNMENTS VIA OPTIMAL TRANSPORT FOR CROSS-DOMAIN FAKE NEWS DETECTION

Fake news causes cognitive misperception among the audience, and spreads panic to the public. It is crucial to detect fake news and prevent its spread early. Previous methods focus on excavating distinguishable features from news contents in a single domain with deep models, which are difficult to generalize to other domains. To solve this problem, News Optimal Transport (NOT) is proposed to learn transferable features across domains by aligning the source and target news using Optimal Transport (OT) techniques. To mitigate issues of heavy computation cost and negative transfer brought by OT, we further propose a mini-batching scheme and a dynamical weighted self-labeling mechanism respectively for model training. Encouraging empirical results on two public benchmarks Politifact and Gossipcop demonstrate that our method outperforms the state-of-the-art methods.