Adaptive Interaction Fusion Networks for Fake News Detection
Table 5: Comparison among different parts of GAIN.
['RumourEval', '-All', 'A(%) 80.24', 'P(%) 77.18', 'R(%) 84.23', 'F1(%) 80.79']
This confirms the effectiveness of each part of GAIN; -Conflicting achieves the worst performance compared to the models that only ablate one part, showing at least 1.29% degradation in accuracy on the two datasets, which explains that conflicting semantics obtained by -Conflicting can more strongly improve the performance of fake news detection.