Abstract
This study used recently released nationally representative data with new measures on health information seeking to estimate the prevalence and predictors of adult social media users’ perceptions of health mis- and disinformation on social media. Most adults who use social media perceive some (46%) or a lot (36%) of false or misleading health information on social media, but nearly one-fifth reported either none or a little (18%). More than two-thirds of participants reported that they were unable to assess social media information as true or false (67%). Our study identified certain population groups that might be a focus of future intervention work, such as participants who use social media to make decisions. The perception by social media users that false and misleading health information on social media is highly prevalent may lend greater urgency to mitigate the spread of false or misleading health information that harms public health.
Keywords: United States, cross-sectional study, consumer health information, misinformation, disinformation
Introduction
False or misleading information has become a major social and public health problem because research has shown that it can spread faster and more broadly than accurate information, and it can cause confusion and mistrust of institutions charged with protecting the public's health.1-5 Misinformation is false or inaccurate information, while disinformation is intentionally disseminating false or inaccurate information.6 Mis- and disinformation on social media have influenced the adoption of harmful behaviors and reduced health-promoting behaviors.7-9 Yet, physicians and scientists attempting to counter mis- and disinformation have been subjected to online harassment.10 Congressional Republicans have recently taken aim at universities and Think Tanks researching disinformation through records requests, subpoenas, and lawsuits.11 Despite these attempts to chill research into disinformation, we need more evidence to counter the growing prevalence of health mis- and disinformation.
Recent studies of health mis- and disinformation have been motivated by anti-vaccine campaigns12-18 and the impact of mis- and disinformation on health behaviors such as smoking and nutrition.19-22 The recent studies on health mis- and disinformation have consisted of innovative and well-designed machine-learning analyses of social media content to extract and classify false information being spread through news sources and social media influencers about the COVID-19 pandemic.23-29 These studies have provided insight into the prevalence of false information on social media, ranging up to 87% of posts, depending on the specific topic.3
Given the recency of the problem of false and misleading social media information and limited nationally representative survey data from social media users, there is a need to better understand social media users’ perception of mis- and disinformation on social media because there is recent evidence that this perception may be a mechanism for harmful beliefs and behaviors.30 To be prepared for the growing prevalence of mis- and disinformation regarding critical health issues, the public health community needs more information about the perceptions of health misinformation on social media among specific populations of social media users.30,31 Therefore, the objective of this study was to use recently released nationally representative data with new measures on health information seeking to estimate the prevalence and predictors of perceptions of false or misleading health information among adult social media users.
Data and methods
Data
This study used cross-sectional survey data from the Health Information National Trends Survey 6 (HINTS 6), which is a nationally representative sample of noninstitutionalized adults 18 years of age and older in the United States sponsored by the National Cancer Institute.32 HINTS 6 data were collected as a mail and online survey from March to November 2022, with a response rate of 28.1%. Participants who had not visited a social media site in the past year or reported that they do not use social media were excluded. After using listwise deletion for cases with missing data, the analytical sample consisted of 3841 adult respondents. HINTS 6 is publicly available with de-identified data; therefore, the university human research protection program deemed it exempt from institutional review board approval. Further details about the survey can be found in methodology reports produced by the National Cancer Institute.32
Measures
The first outcome was assessed by the following question: “How much of the health information that you see on social media do you think is false or misleading?” The response categories were as follows: I do not use social media, none, a little, some, a lot. We dichotomized this measure as none/a little vs some/a lot. Those reporting that they do not use social media were coded as missing. The second outcome asked participants, “I find it hard to tell whether health information on social media is true or false.” The response categories were strongly agree, somewhat agree, somewhat disagree, and strongly disagree, and the responses were recoded as agree or disagree.
Demographic predictors included age (18–49, 50–64, 65+ years), sex (male, female), marital status (married/cohabiting, formerly married, never married), residence in a metro/nonmetropolitan county as designated by the US Department of Agriculture in 2013, race/ethnicity (non-Latino White, non-Latino Black, Latino, non-Latino Asian American/Other), education (high school or less, some college, college degree or higher), full-time employment status, and feelings about household income (finding it very/difficult on present income, getting by on present income, living comfortably on present income).
Predictors of social media use included frequency of visiting social media sites (monthly, weekly, daily) and in the past 12 months ever sharing personal health information, sharing general health-related information, interacting with people who have similar health or medical issues, and watching a health-related video. Other predictors of social media use were asked with a Likert scale and converted to dichotomous measures (agree or disagree): “Most of the people in my social media networks have the same views about health as me,” “I use information from social media in discussions with my health care provider,” and “I use information from social media to make decisions about my health.”
Statistical analysis
All analyses account for survey weights and design using jackknife replicate weights for variance estimation. Statistical significance was defined as a P value < .05. Predictors of perceptions of false or misleading social media information were calculated with multivariable linear probability models and reported as predicted probabilities and 95% confidence intervals. Predictors of perceptions of whether the participant could assess social media information as true or false were calculated with multivariable linear probability models and reported as predicted probabilities and 95% confidence intervals. The supplemental appendix includes the survey-weighted bivariate analyses of the outcomes and predictors in Table A1 and an ordered logit regression for an alternative measurement of perceptions of false or misleading social media information (none/a little, some, a lot) in Table A2.
Results
Table 1 shows the survey-weighted descriptive statistics for the study sample. Most of the sample consisted of individuals aged 18–49 years (60%), female (53%), married or cohabiting (58%), residing in metropolitan areas (88%), identifying their race/ethnicity as non-Latino White (61%), educated beyond high school degree (76%), working full time (61%), and not finding it very difficult on their present income (81%). Most participants visited social media sites daily (74%), did not share personal (81%) or general health information (62%) on social media, did not interact with people with similar health or medical issues on social media (73%), and reported watching a health-related video (70%). Most participants disagreed that most people in social media have the same views about health as the participant (54%) and disagreed that they use social media information in discussion with their health care providers (80%) or to make decisions about their health (84%). The most prevalent perception of the prevalence of false or misleading social media information is some (46%) followed by a lot (36%) and none/a little (18%). More than two-thirds of the sample reported that they were unable to assess social media information as true or false (67%). The most prevalent opinion about who is most responsible for reducing false or misleading social media information was social media companies (33%) followed by individual users/other (25%) and government (15%), medical providers/health care systems (14%), and news media (13%).
Table 1.
Survey-weighted descriptive statistics: Health Information National Trends Survey 6, 2022.
| Raw n | Raw % | Weighted % | |
|---|---|---|---|
| Total n | 3841 | ||
| Outcomes | |||
| How much of the health information that you see on social media do you think is false or misleading? | |||
| None/a little | 715 | 19% | 18% |
| Some | 1768 | 46% | 46% |
| A lot | 1358 | 35% | 36% |
| I find it hard to tell whether health information on social media is true or false | |||
| Disagree | 1303 | 34% | 33% |
| Agree | 2538 | 66% | 67% |
| Demographic predictors | |||
| Age group | |||
| 18–49 y | 1728 | 45% | 60% |
| 50–64 y | 1136 | 30% | 27% |
| 65+ y | 977 | 25% | 13% |
| Birth gender | |||
| Male | 1451 | 38% | 47% |
| Female | 2390 | 62% | 53% |
| Marital status | |||
| Married/cohabiting | 2138 | 56% | 58% |
| Formerly married | 888 | 23% | 10% |
| Never married | 815 | 21% | 33% |
| USDA 2013 rural/urban designation | |||
| Nonmetropolitan | 471 | 12% | 12% |
| Metropolitan | 3370 | 88% | 88% |
| Race/ethnicity | |||
| NH White | 2203 | 57% | 61% |
| NH Black | 591 | 15% | 11% |
| Hispanic | 703 | 18% | 18% |
| NH Asian and other | 344 | 9% | 11% |
| Education | |||
| High school or less | 732 | 19% | 24% |
| Some college | 1105 | 29% | 39% |
| College graduate or higher | 2004 | 52% | 36% |
| Work full time (past 30 days) | |||
| No | 1700 | 44% | 39% |
| Yes | 2141 | 56% | 61% |
| Feelings about household income | |||
| Finding it very/difficult on present income | 757 | 20% | 19% |
| Getting by on present income | 1406 | 37% | 37% |
| Living comfortably on present income | 1678 | 44% | 44% |
| Social media predictors | |||
| Frequency of social media site visits | |||
| Monthly | 529 | 14% | 13% |
| Weekly | 571 | 15% | 14% |
| Daily | 2741 | 71% | 74% |
| Share personal health information on social media past 12 months | |||
| No | 3099 | 81% | 81% |
| Yes | 742 | 19% | 19% |
| Share general health-related information on social media past 12 months | |||
| No | 2347 | 61% | 62% |
| Yes | 1494 | 39% | 38% |
| Interact with people who have similar health or medical issues on social media or online forums past 12 months | |||
| No | 2805 | 73% | 73% |
| Yes | 1036 | 27% | 27% |
| Watch a health-related video on a social media site past 12 months | |||
| No | 1159 | 30% | 30% |
| Yes | 2682 | 70% | 70% |
| Most of the people in my social media networks have the same views about health as me | |||
| Disagree | 2056 | 54% | 54% |
| Agree | 1785 | 47% | 46% |
| I use information from social media in discussions with my health care provider | |||
| Disagree | 3029 | 79% | 80% |
| Agree | 812 | 21% | 20% |
| I use information from social media to make decisions about my health | |||
| Disagree | 3209 | 84% | 84% |
| Agree | 632 | 17% | 16% |
Source: Authors’ analysis of Health Information National Trends Survey 6 (HINTS 6), 2022, which accounts for survey weights and design using jackknife replicate weights for variance estimation.
Abbreviations: NH, non-Hispanic; USDA, US Department of Agriculture.
Table 2 shows the predictors for public perceptions about the prevalence of false or misleading social media information from linear probability models. Latinos (probability = −0.10; 95% CI, −0.17 to −0.04) and non-Latino Black individuals (probability = −0.11; 95% CI, −0.18 to −0.05) were less likely to report a high prevalence of false or misleading social media information compared with non-Latino White individuals. Participants who used social media to make health decisions (probability = −0.10; 95% CI, −0.18 to −0.02) were also less likely to report a high prevalence of false or misleading social media information compared with participants who did not use social media to make health decisions.
Table 2.
Multivariable analysis of adult social media users’ perceptions of social media health information: Health Information National Trends Survey 6, 2022.
| Perceive some/a lot of false or misleading health information on social media | Unable to assess health information on social media as true or false | |||
|---|---|---|---|---|
| Predicted probability | 95% CI | Predicted probability | 95% CI | |
| Age group | ||||
| 18–49 y | ||||
| 50–64 y | 0.01 | −0.04, 0.06 | 0.07 | −0.01, 0.14 |
| 65+ y | 0.01 | −0.05, 0.07 | 0.11 | 0.03, 0.20 |
| Birth gender | ||||
| Male | ||||
| Female | −0.01 | −0.06, 0.03 | 0.00 | −0.04, 0.05 |
| Marital status | ||||
| Married/cohabiting | ||||
| Formerly married | −0.02 | −0.07, 0.03 | 0.00 | −0.07, 0.08 |
| Never married | 0.00 | −0.05, 0.05 | −0.01 | −0.07, 0.04 |
| USDA 2013 rural/urban designation | ||||
| Nonmetropolitan | ||||
| Metropolitan | 0.04 | −0.03, 0.10 | 0.02 | −0.06, 0.10 |
| Race/ethnicity | ||||
| NH White | ||||
| NH Black | −0.10 | −0.17, −0.04 | −0.05 | −0.13, 0.03 |
| Hispanic | −0.11 | −0.18, −0.05 | −0.09 | −0.16, −0.02 |
| NH Asian and other | −0.02 | −0.09, 0.04 | 0.03 | −0.05, 0.11 |
| Education | ||||
| High school or less | ||||
| Some college | 0.05 | −0.02, 0.11 | 0.04 | −0.03, 0.10 |
| College graduate or higher | 0.03 | −0.03, 0.08 | −0.06 | −0.13, 0.01 |
| Work full time (past 30 days) | ||||
| No | ||||
| Yes | 0.00 | −0.04, 0.05 | −0.02 | −0.09, 0.04 |
| Feelings about household income | ||||
| Finding it very/difficult on present income | ||||
| Getting by on present income | 0.02 | −0.04, 0.08 | −0.04 | −0.11, 0.03 |
| Living comfortably on present income | 0.06 | −0.01, 0.13 | −0.05 | −0.11, 0.02 |
| Frequency of social media site visits | ||||
| Monthly | ||||
| Weekly | −0.01 | −0.07, 0.06 | 0.02 | −0.08, 0.12 |
| Daily | 0.01 | −0.04, 0.06 | 0.00 | −0.08, 0.08 |
| Share personal health information on social media past 12 months | ||||
| No | ||||
| Yes | −0.03 | −0.09, 0.03 | −0.01 | −0.09, 0.07 |
| Share general health-related information on social media past 12 months | ||||
| No | ||||
| Yes | −0.01 | −0.06, 0.05 | −0.02 | −0.09, 0.05 |
| Interact with people who have similar health or medical issues on social media or online forums past 12 months | ||||
| No | ||||
| Yes | −0.01 | −0.06, 0.04 | 0.04 | −0.03, 0.11 |
| Watch a health-related video on a social media site past 12 months | ||||
| No | ||||
| Yes | −0.01 | −0.05, 0.04 | −0.01 | −0.06, 0.04 |
| Most of the people in my social media networks have the same views about health as me | ||||
| Disagree | ||||
| Agree | −0.01 | −0.05, 0.04 | 0.10 | 0.05, 0.14 |
| I use information from social media in discussions with my health care provider | ||||
| Disagree | ||||
| Agree | −0.03 | −0.11, 0.05 | 0.01 | −0.05, 0.08 |
| I use information from social media to make decisions about my health | ||||
| Disagree | ||||
| Agree | −0.10 | −0.18, −0.02 | 0.09 | 0.01, 0.17 |
| Constant | 0.80 | 0.69, 0.90 | 0.64 | 0.50, 0.78 |
Total n = 3841. Outcome: “How much of the health information that you see on social media do you think is false or misleading?” Response categories were None/A little vs Some/A lot. Outcome: “I find it hard to tell whether health information on social media is true or false.” Response categories were Disagree or Agree. Predicted probabilities and 95% CIs were estimated with multivariable linear probability regression models.
Abbreviations: NH, non-Hispanic; USDA, US Department of Agriculture.
Table 2 also shows the predictors of whether the participant agrees that they cannot assess social media information as true or false from linear probability models. Participants 65 years and older (probability = 0.11; 95% CI, 0.03–0.20), participants who agreed that most people in social media have the same views about health as the participant (probability = 0.10; 95% CI, 0.05–0.14), and participants who use social media to make health decisions (probability = 0.09; 95% CI, 0.01–0.17) were more likely to report being unable to assess social media information as true or false compared with their reference categories. Latino participants were less likely to report being unable to assess social media information as true or false (probability = −0.09; 95% CI, −0.16 to −0.02) compared with non-Latino White participants.
Discussion
This study found that most adult social media users in the United States reported a high prevalence of false or misleading health information on social media. Our finding that 82% of adult social media users perceived false or misleading health information on social media is consistent with estimates from objective content analyses of social media posts that ranged up to 87%, which suggests that the public is accurately perceiving a high prevalence of health misinformation. However, most social media users also reported that they were unable to assess health information on social media as true or false, which indicates that an area of future inquiry is to better understand why social media users perceive a high prevalence of health misinformation yet claim to be unable to assess the accuracy of health information.33
There were several demographic characteristics that were consistently a predictor for perceptions of misinformation that warrant further consideration in future studies, including age, race/ethnicity, and education. We found that Latinos and non-Latino Black individuals were less likely to report a high prevalence of false or misleading social media information compared with non-Latino White individuals. Moreover, Latinos were less likely to report being unable to assess social media information as true or false. There is growing evidence that historically disadvantaged race or ethnic populations are more likely to receive, consume, and share fake news and mis- and disinformation online compared with the general population.34,35 Possibly, the higher level of engagement in false or misleading information online due to access barriers to accurate information from government sources or medical professionals may shape perceptions of the prevalence of false or misleading social media information. Therefore, supporting the health information needs of historically disadvantaged racial or ethnic persons may require improving access to health care and official government information that is available to persons who experience language or health literacy barriers.36
The findings should be interpreted within the limitations of using cross-sectional survey data. It is important to note that this study represents the first instance in which the public's perceptions of misinformation were included in the HINTS 6 survey; thus, the analyses were restricted to a single cross-section. If this measure is collected in subsequent iterations of HINTS, then trend analyses to detect changes over time may provide additional insights into the prevalence and predictors of false and misleading information.
Conclusion
Our study identified specific social media users that might be a focus of future intervention work, such as participants who use social media to make decisions or who agreed that most people in social media have the same views about health as them. The perception by adult social media users that false and misleading health information on social media is highly prevalent may lend even greater urgency to mitigate the spread of false or misleading health information that harms public health.
Supplementary Material
Contributor Information
Jim P Stimpson, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States.
Alexander N Ortega, Thompson School of Social Work and Public Health, University of Hawaiʻi at Mānoa, Honolulu, HI 96822, United States.
Supplementary material
Supplementary material is available at Health Affairs Scholar online.
Funding
This research was supported by the National Institute on Minority Health and Health Disparities (NIMHD) under award number R01MD018727. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIMHD. The funders had no role in study design, data analysis, decision to publish, or preparation of the manuscript.
Conflicts of interest
Please see ICMJE form(s) for author conflicts of interest. These have been provided as supplementary materials.
Notes
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