Classifying Tweet Level Judgements of Rumours in Social Media
This addresses rumor classification in social media, which is an incremental improvement for researchers and practitioners in misinformation detection.
The paper tackled the problem of classifying tweet-level judgments of rumors in social media by formulating it as a supervised learning task, achieving good results on rumors from the 2011 England riots using multi-task learning.
Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We formulate the problem of classifying tweet level judgements of rumours as a supervised learning task. Both supervised and unsupervised domain adaptation are considered, in which tweets from a rumour are classified on the basis of other annotated rumours. We demonstrate how multi-task learning helps achieve good results on rumours from the 2011 England riots.