Neural Network Architecture for Credibility Assessment of Textual Claims
Table 2: Comparison of CREDO with baselines (LG+SR and CRF).
['[EMPTY]', 'Overall', 'True Claims', 'False Claims', 'Macro- averaged', '[EMPTY]', 'Fake Claims', 'Fake Claims', 'Fake Claims']
K-fold cross validation (K=5) was used for training and testing on Snopes dataset. The evaluation metrics obtained are averaged across all the evaluation sets. CREDO outperforms the state-of-the-art approaches across all the metrics. It is also observed that CREDO shows improvements over other approaches, even without the help of the Web of Trust module. Hence, the combination of Web of Trust and author credibility scoring modules helps in boosting the performance.