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 . There is an increasing need to interpret and act upon rumours spreading quickly through social media , especially in circumstances where their veracity is hard to establish . For instance , during an earthquake in Chile rumours spread through Twitter that a volcano had become active and that there was a tsunami warning in Valparaiso ( Mendoza et al . , 2010 ) . Other examples , from the riots in England in 2011 , were that rioters were going to attack Birmingham 's children hospital and that animals had escaped from the zoo ( Procter et al . , 2013 ) . Social scientists ( Procter et al . , 2013 ) analysed manually a sample of tweets expressing different judgements towards rumours and categorised them manually in supporting , denying or questioning . The goal here is to carry out tweet-level judgement classification automatically , in order to assist in ( near ) real-time rumour monitoring by journalists and authorities ( Procter et al . , 2013 ) . In addition , information about tweet-level judgements has been used as a first step for early rumour detection by ( Zhao et al . , 2015 ) . The focus here is on tweet- ( Qazvinian et al . , 2011 ) or proposed regular expressions as a solution ( Zhao et al . , 2015 ) . We expect posts expressing similar opinions to exhibit many similar characteristics across different rumours . Based on the assumption of a common underlying linguistic signal , we build a transfer learning system that labels newly emerging rumours for which we have little or no annotated data . Results demonstrate that Gaussian Processbased multi task learning allows for significantly improved performance . The novel contributions of this paper are : 1 . Formulating the problem of classifying judgements of rumours in both supervised and unsupervised domain adaptation settings . 2 . Showing how a multi-task learning approach outperforms singletask methods . This paper investigated the problem of classifying judgements expressed in tweets about rumours . First , we considered a setting where no training data from target rumour is available ( LOO ) . Without access to annotated examples of the target rumour the learning problem becomes very difficult . We showed that in the supervised domain adaptation setting ( LPO ) even annotating a small number of tweets helps to achieve better results . Moreover , we demonstrated the benefits of a multi task learning approach , as well as that Brown cluster features are more useful for the task than simple bag of words . Judgement estimation is undoubtedly of great value e.g. for marketing , politics and journalism , helping to target widely believed topics . Although the focus here is on classifying community reactions , Castillo et al . ( 2013 ) showed that community reaction is correlated with actual rumour veracity . Consequently our classification methods may prove useful in the broader and more challenging task of annotating veracity . An interesting direction for future work would be adding non-textual features . For example , the rumour diffusion pattern ( Lukasik et al . , 2015 ) may be a useful cue for judgement classification .

Challenge: Currently, social scientists manually judge and categorize tweets about rumours to analyze, and building automatic systems is challenging without annotated examples.
Approach: They regard the tweet rumour judgement classification problem as a supervised learning task and apply domain adaptation with annotated data from other domains.
Outcome: The evaluation with rumours of England riots shows that the proposed multi-task learning performs well and providing few annotated tweets helps improve results.