Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media

TL;DR

This paper introduces a novel semantic graph-based method to analyze how health-related narratives are framed differently in conspiracy versus mainstream media, revealing distinct thematic focuses.

Contribution

It presents a new approach incorporating narrative information into framing analysis using semantic graphs, addressing gaps in existing NLP-based methods.

Findings

Conspiracy media focus on belief-based framing.

Mainstream media emphasize scientific framing.

Distinct narrative patterns identified between sources.

Abstract

Understanding how online media frame issues is crucial due to their impact on public opinion. Research on framing using natural language processing techniques mainly focuses on specific content features in messages and neglects their narrative elements. Also, the distinction between framing in different sources remains an understudied problem. We address those issues and investigate how the framing of health-related topics, such as COVID-19 and other diseases, differs between conspiracy and mainstream websites. We incorporate narrative information into the framing analysis by introducing a novel frame extraction approach based on semantic graphs. We find that health-related narratives in conspiracy media are predominantly framed in terms of beliefs, while mainstream media tend to present them in terms of science. We hope our work offers new ways for a more nuanced frame analysis.