DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Table 4: Comparison of various approaches for credibility regression on NewsTrust dataset.
['[BOLD] Configuration', '[BOLD] MSE']
When performing credibility regression on the NewsTrust dataset, we evaluate the models in terms of mean squared error (MSE; lower is better) for credibility rating prediction. For CNN-text and LSTM-text, we add a linear fully connected layer as the final layer of the model to support regression. We observe that DeClarE (Full) outperforms all four baselines, with a 17% decrease in MSE compared to the best-performing baselines (i.e., LSTM-text and Distant Supervision). The DeClarE (Plain) model performs substantially worse than the full model, illustrating the value of including attention and source embeddings. CNN-text performs substantially worse than the other baselines.