Adaptive Interaction Fusion Networks for Fake News Detection
Table 4: Comparison between different SFSNs.
['RumourEval', 'SFSN-post-word', 'A(%) 80.05', 'P(%) 76.87', 'R(%) 84.03', 'F1(%) 80.29']
SFSN-post-emotion’, ’SFSN-comment-word’, and ’SFSN-comment-emotion’ are for only removing the fusion on emotion level of posts, only removing the fusion on word level of comments, and only removing the fusion on emotion level of comments, respectively. We have the following observations: The removal of word-level SFSNs presents lower performance than emotion-level SFSNs, which shows up to 1.27% and 1.04% performance degradation than emotion-level SFSNs in accuracy on RumourEval and PHEME, respectively. It reveals that semantics are more effective than emotions for fake news detection. Compared with comment-level SFSNs, post-level SFSNs decrease 0.52% and 0.64% performance, which indicates posts contain more effective credibility indicators. The removal of any SFSN will degrade the performance of the model, which shows from 2.84% to 1.91% degradation in accuracy on RumourEval and PHEME, respectively. This demonstrates the effectiveness of SFSN.