Adaptive Interaction Fusion Networks for Fake News Detection
Table 2: Performance comparison of our proposed model against the baselines.
['Dataset', 'Measure', 'SVM', 'CNN', 'TE', 'DeClarE', 'MTL-LSTM', 'TRNN', 'Bayesian-DL', 'Ours']
We observe that: MTL-LSTM exploiting stance features shows 2.86% and 2.67% improvements in recall on RumourEval and PHEME respectively, but it also introduces noise, which makes it achieve from 4.90% to 21.38% degradation than our model in accuracy, precision, and F1. Besides, our model achieves 1.94% and 2.12% boosts than the latest baseline (Bayesian-DL) in accuracy on the two datasets respectively. These reveal the effectiveness of our model. SVM incorporating meta-data features gains better performance than some deep neural networks only learning content features, like CNN and TE. It confirms the effectiveness of meta-data features for fake news detection. Both TRNN and Bayesian-DL by integrating comments into posts achieves eminent performance than other baselines, which reveals the effectiveness of the fusion of comments and posts. But they are worse than AIFN because AIFN not only captures semantics of comments and posts but also emotional features from the both.