Retain or Reframe? A Computational Framework for the Analysis of Framing in News Articles and Reader Comments
This addresses the gap in NLP methods that ignore the relationship between source content and audience responses, providing tools for large-scale framing analysis in media studies.
The authors tackled the problem of analyzing how news framing influences audience responses by developing the first computational framework that jointly analyzes framing in news articles and reader comments. They found that frame reuse in comments correlates highly across different news outlets while topic-specific patterns vary, and released a well-performing frame classifier and two datasets.
When a news article describes immigration as an "economic burden" or a "humanitarian crisis," it selectively emphasizes certain aspects of the issue. Although \textit{framing} shapes how the public interprets such issues, audiences do not absorb frames passively but actively reorganize the presented information. While this relationship between source content and audience response is well-documented in the social sciences, NLP approaches often ignore it, detecting frames in articles and responses in isolation. We present the first computational framework for large-scale analysis of framing across source content (news articles) and audience responses (reader comments). Methodologically, we refine frame labels and develop a framework that reconstructs dominant frames in articles and comments from sentence-level predictions, and aligns articles with topically relevant comments. Applying our framework across eleven topics and two news outlets, we find that frame reuse in comments correlates highly across outlets, while topic-specific patterns vary. We release a frame classifier that performs well on both articles and comments, a dataset of article and comment sentences manually labeled for frames, and a large-scale dataset of articles and comments with predicted frame labels.