DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Table 3: Comparison of various approaches for credibility classification on Snopes and PolitiFact datasets.
['[BOLD] Dataset', '[BOLD] Configuration', '[ITALIC]  [BOLD] True Claims Accuracy (%)', '[ITALIC]  [BOLD] False Claims Accuracy (%)', '[BOLD] Macro F1-Score', '[BOLD] AUC']
DeClarE outperforms LSTM-text and CNN-text models by a large margin on both datasets. On the other hand, for the Snopes dataset, performance of DeClarE (Full) is slightly lower than the Distant Supervision configuration (p-value of 0.04 with a pairwise t-test). However, the advantage of DeClarE over Distant Supervision approach is that it does not rely on hand crafted features and lexicons, and can generalize well to arbitrary domains without requiring any seed vocabulary. It is also to be noted that both of these approaches use external evidence in the form of reporting articles discussing the claim, which are not available to the LSTM-text and CNN-text baselines. This demonstrates the value of external evidence for credibility assessment.