Online media shapes public opinion, with messages from opposing information sources deepening polarization. How content is framed and presented determines its meaning and impact on readers. To date, studies on framing have focused on finding certain phrases, topics, or ideas characterizing messages from particular sources, a strategy providing only limited results. In this study, we used a broader, agnostic approach that involves extracting the narrative from text, including its characters, plot, setting, and moral of the story, and the relationships between these elements. We translated text from tens of thousands of health documents in the language of conspiracy corpus (LOCO) into representations called semantic graphs . We then compared these graphs across documents from conspiracy and mainstream sources. We found that conspiracy media framed health through belief, emphasizing immediacy and individual impact, whereas mainstream media used scientific framing with a long-term, institutional focus. Shared words carried divergent narratives. As one example, in documents related to COVID-19, conspiracy media emphasized preventing violence rather than preventing infection (the mainstream narrative). Understanding conspiracy narratives is crucial for policymakers and media platforms seeking to curb their spread. This understanding can inform efforts to boost media literacy and reduce the number of posts peddling misinformation. We suggest using automated tools and AI as an aid in both efforts.
Reiter-Haas et al. (Wed,) studied this question.
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