DEAN: Learning Dual Emotion for Fake News Detection on Social Media
Table 3: Performance Comparison of Fake News Detection on Two Datasets.
['Dataset', 'Methods', 'Accuracy', 'Precision', 'Recall', 'F1-Score']
In particular, the DEAN model achieves an overall accuracy of 87.2% on Weibo dataset and 75.1% on Twitter dataset, which outperforms all the baseline models on both datasets. The outstanding performance of the proposed model demonstrates that the incorporation of emotion through embedding representation and gated fusion could effectively promote the detecting process on fake news. Besides, compared to the emotion-based model SAME, DEAN earns quite better performance in all aspects. On the one hand, it is because that SAME is mainly designed to deal with heterogeneous multi-modal data. On the other hand, the results demonstrate that only utilizing user comments’ emotion just like SAME is limited, and DEAN is more effective for detecting fake news by exploiting dual emotion from publisher and users corporately. Our method shows its strength on fake news detection in these experiments. And its f1-score is also 3.4% higher than the second one. On Twitter dataset, the improvement is more obvious by boosting the accuracy from 61.3% of feature-based models to 75.1%. Meanwhile, DEAN outperforms the second-best model by nearly 6% in f1-score. These observations demonstrate the importance of incorporating emotion information into models. By the way, comparing between datasets, we can see that performances on Weibo dataset are rather better than Twitter.