Who Should Bear the Responsibility for Combating Health Misinformation on Social Media? An Analysis of Sociodemographic Factors Using HINTS 6 Data

Gulzar H. Shah, Indira K. Karibayeva

Societies, 2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.3390/soc16110364
Data confidence 70 medium
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Abstract

Background/Objectives: Health misinformation on social media is a growing public health concern, yet less is known about whom the public holds primarily responsible for reducing it. This study examined sociodemographic differences in U.S. adults’ perceived responsibility for reducing health misinformation on social media. Methods: This cross-sectional study used nationally representative data from the Health Information National Trends Survey 6 (HINTS 6, 2022). Respondents were asked who has the main responsibility for reducing false or misleading health information on social media. Descriptive analyses reported unweighted counts and weighted percentages. Survey-weighted multinomial logistic regression with jackknife replicate variance estimation was used to examine associations with age, sex assigned at birth, race/ethnicity, education, marital status, full-time work status, and household income, and design-adjusted Wald tests assessed the overall association of each characteristic. Results: Among 6252 respondents, the largest weighted proportion attributed responsibility to social media platforms such as Facebook, Twitter, or YouTube (31.40%), followed by individual social media users (18.55%), the government (15.87%), medical providers and health care systems (13.79%), the news media (12.60%), and other sources (3.97%). In the adjusted model (n = 5306), age (p = 0.032) and education (p = 0.014) were associated with perceived responsibility overall. Compared with adults aged 65 years or older, adults aged 18–34 years had lower odds of naming platforms (AOR = 0.59, 95% CI: 0.41–0.85), the government (AOR = 0.57, 95% CI: 0.35–0.93), the news media (AOR = 0.48, 95% CI: 0.26–0.87), and medical providers and health care systems (AOR = 0.31, 95% CI: 0.19–0.52) rather than individual users, and adults aged 35–49 years showed a similar pattern. Adults with less than a high school education had higher odds than college graduates of selecting the government (AOR = 3.13, 95% CI: 1.45–6.76), the news media (AOR = 3.24, 95% CI: 1.15–9.14), and platforms (AOR = 2.12, 95% CI: 1.03–4.38). In sensitivity analyses, the direction of the main age and education estimates was broadly consistent; however, the overall age association was not statistically significant with an alternative age classification (p = 0.321) or among social media users only (p = 0.071). Conclusions: Perceived responsibility for reducing health misinformation on social media differs mainly by age and education, although statistical evidence for the age association depended on the age classification and the population analyzed. As a policy implication, the findings support a shared-responsibility approach involving platforms, public health agencies, health care systems, news media, and users; whether tailoring interventions to these sociodemographic differences improves their effectiveness should be tested in future studies.