Others Are to Blame: Whom People Consider Responsible for Online Misinformation
Gabriel Lima, Jiyoung Han, Meeyoung Cha
Abstract
Determining who is responsible for online misinformation is an important problem. This research offers a multifaceted view of the public's perception of who is responsible for online misinformation. Via two studies, we surveyed how people attribute responsibility separately for creating, disseminating, and failing to prevent the dissemination of false information online. Study 1 (N=99) employed a mixed-methods approach to identify a series of actors deemed responsible for each aspect of misinformation. Its open-ended methodology suggested that participants tended to externalize responsibility, which we explored further in the subsequent study. Study 2 (N=496) found that the responsible entities differed for the three distinct aspects of misinformation: online users, news media, and interest groups were associated with creating falsehoods, whereas social media platforms were predominantly seen as accountable for failing to prevent them. Our data shows that blame was directed towards those on the opposite side of the political spectrum, indicating substantial polarization. Most critically, people did not seem to associate themselves with online misinformation and externalized responsibility towards "other users." We discuss implications, including the need to promote personal accountability among users and the social demand for accountable social media platforms and news media.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Your agent calls
Lunesearch_papers
Free to start. No credit card required.
Terminal
Install the CLIlune papers get e718656d-04b7-45b0-90e6-0a3e31731df1Cited by top-tier papers1
Ask how each one uses itRelated papers
- Perverse Downstream Consequences of Debunking: Being Corrected by Another User for Posting False Political News Increases Subsequent Sharing of Low Quality, Partisan, and Toxic Content in a Twitter Field ExperimentMohsen Mosleh, Cameron Martel, Dean Eckles, David G. RandCHI 2021 · 109 citations
- Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan MisinformationChenyan Jia, Alexander Boltz, Angie Zhang, Anqing Chen et al.CSCW 2022 · 47 citations
- That's Fake News! Reliability of News When Provided Title, Image, Source Bias & Full ArticleFrancesca Spezzano, Anu Shrestha, Jerry Alan Fails, Brian W. StoneCSCW 2021 · 22 citations
- Countering Fake News: A Comparison of Possible Solutions Regarding User Acceptance and EffectivenessJan Kirchner, Christian ReuterCSCW 2020 · 89 citations
- Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based ProcessesAlexandros Efstratiou, Emiliano De CristofaroCSCW 2022 · 23 citations
