Fake News Detection Based on Knowledge-Guided Semantic Analysis

Recently, fake news, such as low-quality news with intentionally false information, has threatened the authenticity of news information. However, existing detection methods are inefficient in modeling complicated data and leveraging external knowledge. To address these limitations, we propose a fake news detection framework based on knowledge-guided semantic analysis, which compares the news to external knowledge through triplets for fake news detection. Considering that equivalent elements of triplets may be presented in different forms, a triplet alignment method is designed to construct the bridge between news documents and knowledge graphs. Then, a dual-branch network is developed to conduct interaction and comparison between text and knowledge embeddings. Specifically, text semantics is analyzed with the guidance generated by a triplet aggregation module to capture the inconsistency between news content and external knowledge. In addition, a triplet scoring module is designed to measure rationality in view of general knowledge as a complementary clue. Finally, an interaction module is proposed to fuse rationality scores in aspects of text semantics and external knowledge to obtain detection results. Extensive experiments are conducted on publicly available datasets and several state-of-the-art methods are considered for comparison. The results verify the superiority of the proposed method in achieving more reliable detection results of fake news.