Interpretable Rumor Detection in Microblogs by Attending to User Interactions
Table 1: Average tree depths, number of leaves and tweets.
['Data set', 'Twitter15', 'Twitter16', 'PHEME']
We found that in social media, as often the entire thread is visible, a user replying to the root post might be continuing the conversation of earlier users and not specifically writing a reply to the root post. Tree models that do not explicitly model every possible pairwise interaction between tweets are therefore sub-optimal in modelling social media conversations. Therefore, we propose to overcome the limitations with our described models in subsequent sections. We evaluate our model based on two rumour detection data sets - (i) Twitter15 and Twitter16 and (ii) PHEME 5 events data set. For the PHEME 5 data set, we follow the experimental setting of \citeauthorkumar19 \shortcitekumar19 in using event wise cross-validation. For the Twitter15 and Twitter 16 data sets, there is a large proportion of retweets in each claim: 89% for Twitter15 and 90% for Twitter16. As we assume that retweets do not contribute new information to the model, we removed all retweets for Twitter15 and Twitter16. After the removal of retweets, we observe that a small number of claims would be left with only the source tweet. Since the principle behind our methodology is that we could exploit crowd signals for rumor detection, claims without any replies should then be “unverified”. Hence, we amended the labels for such claims to be “unverified” in the training data (1.49% of Twitter15 - 8 False, 10 True and 3 Non-Rumour. 1.46% of Twitter16 - 9 False, 2 True and 0 Non-Rumour). In order for our results to be comparable with previous work, we excluded such claims from our testing set.