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Social media is a rich source of rumours and corresponding communityreactions. Rumours reflect different characteristics, some shared and someindividual. We formulate the problem of classifying tweet level judgements ofrumours as a supervised learning task. Both supervised and unsupervised domainadaptation are considered, in which tweets from a rumour are classified on thebasis of other annotated rumours. We demonstrate how multi-task learning helpsachieve good results on rumours from the 2011 England riots.

Classifying Tweet Level Judgements of Rumours in Social Media