Discovering Differential Features: Adversarial Learning for Information Credibility Evaluation
Table 4: The performance evaluation of ANSP on LIAR and Weibo
['[EMPTY]', 'LIAR  [BOLD] A', 'LIAR  [BOLD] P', 'LIAR  [BOLD] R', 'LIAR  [BOLD] F1', 'Weibo  [BOLD] A', 'Weibo  [BOLD] P', 'Weibo  [BOLD] R', 'Weibo  [BOLD] F1']
In details, the model has a 17% average improvement compared with LR and SVMs because our model with neural networks can fully exploit deep semantic representations of text. The model achieves the state-of-the-art performance on two datasets with 42.8%, and 46.3% overall accuracy respectively, surpassing CNN, LSTM, and BiLSTM by a significant margin of 16.8%, and 14.2%. This suggests that private features and common features captured by our model based on multi-task learning are effective for credibility evaluation. The model outperforms attention-based method with 2.1%, 3.1% improvements. This is because our model reduces common, irrelevant-type features from private features and focuses on the differential credibility features.