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In recent times, social media sites such as Twitter have been extensivelyused for debating politics and public policies. These debates span millions oftweets and numerous topics of public importance. Thus, it is imperative thatthis vast trove of data is tapped in order to gain insights into public opinionespecially on hotly contested issues such as abortion, gun reforms etc. Thus,in our work, we aim to gauge users' stance on such topics in Twitter. Wepropose ReLP, a semi-supervised framework using a retweet-based labelpropagation algorithm coupled with a supervised classifier to identify userswith differing opinions. In particular, our framework is designed such that itcan be easily adopted to different domains with little human supervision whilestill producing excellent accuracy

Identifying Users with Opposing Opinions in Twitter Debates