Sentence-Guided Comment Tree Fusion for Fake News Detection

The rapid spread of fake news significantly impacts social cognition and media credibility, making the effective detection of fake news a critical issue. This paper proposes a fake news detection method based on a comment tree structure. By constructing a hierarchical comment tree and integrating cosine similarity filtering with a cross-attention mechanism, the method optimizes the comment selection process. Finally, a gated mechanism is employed to fuse the features of news sentences and comment information, thereby improving the accuracy of fake news detection. Experiments conducted on two public datasets, FakeNewsNet and BuzzFace, demonstrate that the proposed method achieves significant performance improvements in fake news detection compared to baseline models.