Unsupervised User Stance Detection on Twitter

Understand

We present a highly effective unsupervised framework for detecting the stance of prolific Twitter users with respect to controversial topics.

• In particular, we use dimensionality reduction to project users onto a low-dimensional space, followed by clustering, which allows us to find core users that are representative of the different stances.
• Our framework has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification.
• First, we do not require any prior labeling of users: instead, we create clusters, which are much easier to label manually afterwards, e.g., in a matter of seconds or minutes instead of hours.