Discovering Differential Features: Adversarial Learning for Information Credibility Evaluation
Table 7: The performance evaluation of ANSP on Twitter16
['Method', '[BOLD] Accuracy', '[BOLD] NR', '[BOLD] FR', '[BOLD] TR', '[BOLD] UR']
Specifically, the performance on types “true” and “false” is more remarkable than types “half-true”, “half-false”, etc. The reason is that our model discovers differential features through separating common features between true and false information. But this is coarse-grained due to ignoring the differential features between half-true and half-true types, which is a limitation to our model. In the future, to address this problem, we will focus on capturing the differential features among multiple types to improve the performance of different credibility types of information.