Interpretable Rumor Detection in Microblogs by Attending to User Interactions
Table 5: Samples of tweet level explanation for each claim. We sort tweets based on the number of times it was identified as the most relevant tweet to the most important tweet and show the top three tweets. The right most column gives the number of tweets in the thread.
['[BOLD] (Label) Claim (Unverified) Surprising number of vegetarians secretly eat meat', '[BOLD] Important Tweets 1', '[BOLD] Important Tweets @HuffingtonPost …….. then they aren’t vegetarians.', '[BOLD] #Tweets 33']
The amount of information a tweet is propagating to another tweet is weighted by the relatedness between the pair, measured by the self-attention weight between them - higher self-attention weight imply higher relatedness. We then use the attention mechanism to interpolate tweets in the final layer before the prediction layer. This generates a representation vector for the claim that would be used for prediction. The attention weight for each tweet indicates the level of importance the model has placed on that tweet for prediction. Higher attention weight implies higher importance. Therefore, to generate the explanations for each claim, we obtain the tweet with the highest attention weight at the final layer - this would give us the most important tweet, tweetimpt, for prediction. After which, we obtained the most relevant tweet, tweetrel,i to tweetimpt at the ith MHA layer. We do so by obtaining tweets with highest self-attention weight with this particular tweet at each MHA layer. The same tweet could be identified as the most relevant tweet multiple times. We ranked each tweet based on the number of times it was identified as a relevant tweet. The top three tweets would be the explanation for this particular claim. These replies were accurately identified and used by our model in debunking or confirming the factuality of claims despite a large number of other tweets present in the conversation.