Estimating collective judgement of rumours in social media
Social media is a rich source of rumours and corresponding community reactions. Determining the extent of community belief in a rumour is of value for marketing, politics and journalism. Moreover, collective judgements have been shown to correlate with ground truth. We formulate the problem of estimating the collective judgement of a rumour in social media as a supervised learning using annotated examples. Rumours reflect different characteristics, some shared and some individual. We consider both supervised and unsupervised domain adaptation, in which rumour predictions are made on the basis of other annotated rumours. Evaluation on a set of several thousands tweets on seven rumours shows that we can successfully predict individual and collective judgements.
ArXiv