Classifying Tweet Level Judgements of Rumours in Social Media
M Lukasik, T Cohn, K Bontcheva
The Association for Computational Linguistics | Published : 2015
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.