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Stance detection entails ascertaining the position of a user towards atarget, such as an entity, topic, or claim. Recent work that employsunsupervised classification has shown that performing stance detection on vocalTwitter users, who have many tweets on a target, can yield very high accuracy(+98%). However, such methods perform poorly or fail completely for less vocalusers, who may have authored only a few tweets about a target. In this paper,we tackle stance detection for such users using two approaches. In the firstapproach, we improve user-level stance detection by representing tweets usingcontextualized embeddings, which capture latent meanings of words in context.We show that this approach outperforms two strong baselines and achieves 89.6%accuracy and 91.3% macro F-measure on eight controversial topics. In the secondapproach, we expand the tweets of a given user using their Twitter timelinetweets, and then we perform unsupervised classification of the user, whichentails clustering a user with other users in the training set. This approachachieves 95.6% accuracy and 93.1% macro F-measure.

A Few Topical Tweets are Enough for Effective User-Level Stance Detection