Characterizing Online Criticism of Partisan News Media Using Weakly Supervised Learning
2026-06-02 • Social and Information Networks
Social and Information Networks
AI summaryⓘ
The authors created new ways to find tweets that criticize news sources with clear political biases. Since there isn’t much labeled data for this task, they used a learning method that relies on noisy signals from tweet content and users' past news sharing. They found that criticism of the media spikes during major political events and is often linked to users exposed to unreliable or very partisan news. Their work helps us better understand how people criticize the media over time and across different news sources.
partisan newsmedia criticismweakly supervised learninghyperpartisanshipmisinformationfilter bubblespolitical polarizationTwitter analysisnoisy labeling functionsnews sharing behavior
Authors
Karthik Shivaram, Mustafa Bilgic, Matthew Shapiro, Aron Culotta
Abstract
We propose novel methods to identify tweets that criticize partisan news sources. Prior work suggests that criticism, ridicule, and distrust of news media all play important roles in hyperpartisanship, misinformation, and filter bubble formation. Thus, understanding the prevalence and temporal dynamics of media-targeted criticism can provide us with updated tools to assess the health of the information ecosystem. There is a scarcity of labeled data for this task, and we develop a weakly supervised learning approach that leverages multiple noisy labeling functions based on both the content of the tweet as well as the historical news sharing behavior of the user. Using this classifier, we explore how tweets expressing criticism vary by user, news source, and time, finding substantial spikes in media criticism during politically polarizing events, such as the investigation into Russian interference in the 2016 U.S.~elections and the 2017 ``unite the right'' rally in Charlottesville. This type of media-targeting criticism is also more likely to occur after users have been exposed to unreliable and hyperpartisan media.