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

arXiv: https://arxiv.org/abs/2401.10030 · HTML: https://mlsanity.com/paper/2401.10030/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Improving AMR Parsing with Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2010.01771/) (2020)
- [Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter](https://mlsanity.com/paper/2004.06986/) (2020)
- [Political Framing: US COVID19 Blame Game](https://mlsanity.com/paper/2007.09655/) (2020)
- [Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter](https://mlsanity.com/paper/2103.06960/) (2021)
- [Studying Moral-based Differences in the Framing of Political Tweets](https://mlsanity.com/paper/2103.11853/) (2021)
- [AMR Normalization for Fairer Evaluation](https://mlsanity.com/paper/1909.01568/) (2019)

## Built on by (most cited first, 2 of 2)

- [The TEA Nets framework combines AI and cognitive network science to model targets, events and actors in text](https://mlsanity.com/paper/2604.27673/) (2026)
- [Automated Identification of Competing Narratives in Political Discourse on Social Media](https://mlsanity.com/paper/2609.11202/) (2026)

## Abstract (arXiv)

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.