Predicting the Topical Stance of Media and Popular Twitter Users
This work addresses the need for social statisticians and policymakers to discover stances on debatable topics without costly manual annotation, though it is incremental as it builds on existing supervised solutions.
The paper tackled the problem of predicting the topical stance of media and popular Twitter users by proposing a cascaded method that combines unsupervised learning on retweet behavior and supervised learning on user labels, achieving 82.6% accuracy compared to gold labels.
Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers. Many supervised solutions exist for determining viewpoints, but manually annotating training data is costly. In this paper, we propose a cascaded method that uses unsupervised learning to ascertain the stance of Twitter users with respect to a polarizing topic by leveraging their retweet behavior; then, it uses supervised learning based on user labels to characterize both the general political leaning of online media and of popular Twitter users, as well as their stance with respect to the target polarizing topic. We evaluate the model by comparing its predictions to gold labels from the Media Bias/Fact Check website, achieving 82.6% accuracy.