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Identifying user stance related to a political event has severalapplications, like determination of individual stance, shaping of publicopinion, identifying popularity of government measures and many others. Thehuge volume of political discussions on social media platforms, like, Twitter,provide opportunities in developing automated mechanisms to identify individualstance and subsequently, scale to a large volume of users. However, issues likeshort text and huge variance in the vocabulary of the tweets make such exerciseenormously difficult. Existing stance detection algorithms require either eventspecific training data or annotated twitter handles and therefore, aredifficult to adapt to new events. In this paper, we propose a sign networkbased framework that use external information sources, like news articles tocreate a signed network of relevant entities with respect to a news event andsubsequently use the same to detect stance of any tweet towards the event.Validation on 5,000 tweets related to 10 events indicates that the proposedapproach can ensure over 6.5% increase in average F1 score compared to theexisting stance detection approaches.

Detecting Stance in Tweets : A Signed Network based Approach