Quantifying the impact of misinformation and vaccine-skeptical content on Facebook

• 成果类型: Article
• 署名作者: Allen, Jennifer; Watts, Duncan J.; Rand, David G.
• 署名单位: Massachusetts Institute of Technology (MIT); University of Pennsylvania; University of Pennsylvania; University of Pennsylvania; Massachusetts Institute of Technology (MIT); Massachusetts Institute of Technology (MIT)
• 刊物名称: SCIENCE
• ISSN/ISSBN: 0036-9598
• DOI: 10.1126/science.adk3451
• 发表日期: 2024-05-31
• 关键词: fake news; political news; false news; BEHAVIOR; exposure

摘要

Low uptake of the COVID-19 vaccine in the US has been widely attributed to social media misinformation. To evaluate this claim, we introduce a framework combining lab experiments (total N = 18,725), crowdsourcing, and machine learning to estimate the causal effect of 13,206 vaccine-related URLs on the vaccination intentions of US Facebook users (N approximate to 233 million). We estimate that the impact of unflagged content that nonetheless encouraged vaccine skepticism was 46-fold greater than that of misinformation flagged by fact-checkers. Although misinformation reduced predicted vaccination intentions significantly more than unflagged vaccine content when viewed, Facebook users' exposure to flagged content was limited. In contrast, unflagged stories highlighting rare deaths after vaccination were among Facebook's most-viewed stories. Our work emphasizes the need to scrutinize factually accurate but potentially misleading content in addition to outright falsehoods.