KAPALM: Knowledge grAPh enhAnced Language Models for Fake News Detection

Abstract

Social media has not only facilitated news consumption, but also led to the wide spread of fake news. Because news articles in social media is usually condensed and full of knowledge entities, existing methods of fake news detection use external entity knowledge. However, majority of these methods focus on news entity information and ignore the structured knowledge among news entities. To address this issue, in this work, we propose a Knowledge grAPh enhAnced Language Model (KAPALM) which is a novel model that fuses coarse- and fine-grained representations of entity knowledge from Knowledge Graphs (KGs). Firstly, we identify entities in news content and link them to entities in KGs. Then, a subgraph of KGs is extracted to provide structured knowledge of entities in KGs and fed into a graph neural network to obtain the coarse-grained knowledge representation. This subgraph is pruned to provide fine-grained knowledge and fed into the attentive graph and graph pooling layer. Finally, we integrate the coarse- and fine-grained entity knowledge representations with the textual representation for fake news detection. The experimental results on two benchmark datasets show that our method is superior to state-of-the-art baselines. In addition, it is competitive in the few-shot scenario.