2024-09-30 ニューヨーク大学 (NYU)
Photo credit: Alicja Nowakowska/Getty Images
＜関連情報＞
- https://www.nyu.edu/about/news-publications/news/2024/september/online-misinformation-most-likely-to-be-believed-by-ideological-.html
- https://academic.oup.com/pnasnexus/advance-article/doi/10.1093/pnasnexus/pgae396/7754768
ソーシャルメディア・プラットフォームにおける誤報の受容度を大規模に測定 Measuring receptivity to misinformation at scale on a social media platform
Christopher K Tokita, Kevin Aslett, William P Godel, Zeve Sanderson, Joshua A Tucker, Jonathan Nagler, Nathaniel Persily, Richard Bonneau
PNAS Nexus Published:10 September 2024
DOI:https://doi.org/10.1093/pnasnexus/pgae396
Abstract
Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation’s initial spread. Our paper provides a more precise estimate of misinformation’s impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.
