Predicting the Topical Stance and Political Leaning of Media using Tweets

Peter Stefanov, Kareem Darwish, Atanas Atanasov, Preslav Nakov


Abstract
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.
Anthology ID:
2020.acl-main.50
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
527–537
Language:
URL:
https://aclanthology.org/2020.acl-main.50
DOI:
10.18653/v1/2020.acl-main.50
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.50.pdf
Video:
 http://slideslive.com/38929386