# Predicting the Topical Stance of Media and Popular Twitter Users

arXiv: https://arxiv.org/abs/1907.01260 · HTML: https://mlsanity.com/paper/1907.01260/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning](https://mlsanity.com/paper/2202.12382/) (2022)

## Abstract (arXiv)

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.