  Stance detection entails ascertaining the position of a user towards a
target, such as an entity, topic, or claim. Recent work that employs
unsupervised classification has shown that performing stance detection on vocal
Twitter users, who have many tweets on a target, can yield very high accuracy
(+98%). However, such methods perform poorly or fail completely for less vocal
users, 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 first
approach, we improve user-level stance detection by representing tweets using
contextualized 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 second
approach, we expand the tweets of a given user using their Twitter timeline
tweets, and then we perform unsupervised classification of the user, which
entails clustering a user with other users in the training set. This approach
achieves 95.6% accuracy and 93.1% macro F-measure.
