DEAN: Learning Dual Emotion for Fake News Detection on Social Media
Table 4: Analysis of the Dual Emotion Modeling.
['Module', 'Methods', 'Weibo Dataset Accuracy', 'Weibo Dataset F1-Score', 'Twitter Dataset Accuracy', 'Twitter Dataset F1-Score']
, we could make the following observations: 1) in content module, the overall performance rises while using emotion embeddings; especially on Twitter dataset, adding emotion information increases the f1-score by nearly 6%; 2) publisher emotion plays a more important role than social emotion on both datasets. It possibly results from the sparsity of comments data, which limits the effectiveness of social emotion; 3) compared to merely using semantic information, incorporation of emotion from one side or two sides all improve the performance of the whole framework, which demonstrates the importance of dual emotion on fake news detection.