Framing Analysis of Health-Related Narratives: Conspiracy versus
  Mainstream Media
Markus Reiter-Haas Beate Klösch Markus Hadler Elisabeth Lex
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.