Explainable Rumor Detection using Inter and Intra-feature Attention Networks
Table 5: Attention values for content latent features (USE embedding), handcrafted content features and user features for three records numbered 857, 870 and 94. Label 0 corresponds to a false rumor and Label 1 corresponds to a true rumor. In all cases, the model identified the status of the tweet correctly.
['[BOLD] Record Number', '857', '870', '94']
Finally we show how to interpret the relative importance between the feature classes themselves (user features, handcrafted features and latent features) using an example. For record number 94, (identified correctly as the true rumor) the model gave 30.8% of the weight to latent features, 44.7% weight to handcrafted content features and 24.5% weight to the user features. This level of explanations can give the user a better idea of what features of the tweet or news item triggered the model to arrive at its conclusion.