Interpretable Rumor Detection in Microblogs by Attending to User Interactions
Table 4: Percentage of claims containing each word.
['Words', 'Twitter15', 'Twitter16', 'PHEME']
To investigate why StA-HiTPLAN was the best performer for PHEME, we hypothesize that the signals for rumor detection in PHEME could be much weaker or expressed in a more implicit manner. Therefore, it would be necessary to study the interaction between the words to better capture the meaning of the whole sentence. To this end, we identified individual words that could be useful in determining veracity of the claim. These words act as a proxy for crowd signals for the model to learn from and we observe that the usage of such words is the lowest in PHEME. Therefore, these observations suggests that there is weaker crowd signal in PHEME. Hence, token level attention might have been necessary to do well in the PHEME. The important tweet shown in the example is however not straightforward and would require inferring that ”doesn’t need facts to write stories” would imply that the claim is fake. Therefore, token-level self-attention is required to accurately capture the meaning of the phrase.