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Classifying Tweet Level Judgements of Rumours in Social Media
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
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