Discovering Differential Features: Adversarial Learning for Information Credibility Evaluation
Table 6: The performance of different methods for common features extraction
['Adversarial Networks', '[BOLD] A', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
Furthermore, in order to evaluate the effectiveness of reinforcement learning in common feature extractor, we design two methods, i.e., the method of using adversarial networks with reinforcement learning and the method without reinforcement learning, to demonstrate its performance. We observe that the method of using adversarial networks with reinforcement learning achieves better performance, outperforming the latter with 3.7% on accuracy, which indicates that reinforcement learning contributes to capturing common features from true and false information.