This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.
As social media platforms have become significant sources of information during the pandemic, a significant volume of both factual and inaccurate information related to the prevention of COVID-19 has been disseminated through social media. Thus, disparities in COVID-19 information verification across populations have the potential to promote the dissemination of misinformation among clustered groups of people with similar characteristics.
This study aimed to identify the characteristics of social media users who obtained COVID-19 information through unofficial social media accounts and were (1) most likely to change their health behaviors according to web-based information and (2) least likely to actively verify the accuracy of COVID-19 information, as these individuals may be susceptible to inaccurate prevention measures and may exacerbate transmission.
An online questionnaire consisting of 17 questions was disseminated by West China Hospital via its official online platforms, between May 18, 2020, and May 31, 2020. The questionnaire collected the sociodemographic information of 14,509 adults, and included questions surveying Chinese netizens’ knowledge about COVID-19, personal social media use, health behavioral change tendencies, and cross-verification behaviors for web-based information during the pandemic. Multiple stepwise regression models were used to examine the relationships between social media use, behavior changes, and information cross-verification.
Respondents who were most likely to change their health behaviors after obtaining web-based COVID-19 information from celebrity sources had the following characteristics: female sex (
The findings suggest that governments, health care agencies, celebrities, and technicians should combine their efforts to decrease the risk in vulnerable groups that are inclined to change health behaviors according to web-based information but do not perform any fact-check verification of the accuracy of the unofficial information. Specifically, it is necessary to correct the false information related to COVID-19 on social media, appropriately apply celebrities’ star power, and increase Chinese netizens’ awareness of information cross-verification and eHealth literacy for evaluating the veracity of web-based information.
Because of the unprecedented magnitude of the COVID-19 pandemic and initial uncertainty about the virus, strategies, such as maintaining social distance and frequent hand washing, were deemed to be the most effective and feasible countermeasures [
Because the pandemic put individuals at high risk of infection and created a situation of great uncertainty, individuals experienced high levels of concern and anxiety. Thus, they began to seek help through the most accessible avenues available to them, namely, social media [
Previous studies have found that social media can be used to disseminate health improvement measures [
Individuals with access to various sources of COVID-19 information are more likely to be knowledgeable about the correct preventive measures, which facilitates appropriate health behavioral changes [
The original contribution of this study is related to its aim to increase knowledge of the behaviors of Chinese netizens during the pandemic by addressing some gaps in the literature. In particular, this study identified the characteristics of Chinese netizens who primarily obtain COVID-19 information from unofficial social media and who are (1) more likely to change their health behaviors based on information from unofficial social media and (2) inclined to directly change their health behaviors without cross-referencing the veracity of web-based information released by unofficial sources.
West China Hospital (WCH), Sichuan University, is one of the largest single-site hospitals in the world, ranking second among general hospitals in China [
The study was approved by the Research Ethics Committee of WCH. The manuscript adhered to the reporting standards outlined by the Checklist for Reporting the Results of Internet E-Surveys (CHERRIES) and the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [
The questionnaire was created on the online survey platform
The authors initially developed a questionnaire that contained 21 questions based on a literature review of relevant studies, as well as World Health Organization materials on COVID-19 [
To ensure the validity of the questionnaire, 20 experts from different fields were selected from the Sichuan Provincial health service system, including respiratory physicians, epidemiologists, medical informaticists, and health care policy-makers. The questionnaire was evaluated by the panel of experts to validate its content with intended constructs and theories. The content validity of the questionnaire was assessed by the item-level content validity index (CVI), which was measured on a 4-point Likert scale, including different parameters such as relevance, clarity, simplicity, and ambiguity [
A set of sociodemographic variables was collected in the first section of the questionnaire, including gender, age (referenced from the categorization by the National Bureau of Statistics of China), educational status, occupation (referenced from the standard occupational classification in China [
Social media use was measured by the amount of time (in hours) spent on social media per day and the frequency of searching for information related to COVID-19. A multiple-choice question was asked about which of the 5 types of accounts were preferred when searching for information about COVID-19 on social media. To measure the trustworthiness of a specific source of web-based information on social media, the participants were asked to rate the perceived trustworthiness of each type of information source using a 5-point Likert scale from 1 (least trustworthy) to 5 (most trustworthy).
Participants’ basic knowledge of COVID-19 was evaluated using 4 questions developed based on the COVID-19 Protection Manual (China Mainland Version, January 2020), including 1 multiple-choice question related to COVID-19 transmission and 3 single-choice questions centered around the proper use of masks. Each correct answer was assigned 1 point, and incorrect answers were assigned 0 points for a maximum of 6 points.
To measure whether the individuals would change their health behaviors, participants were asked, “Did you change health behaviors based on the COVID-19 information on social media?” with answer options “Yes” and “No.” Subsequently, a question (“Did you cross-verify the authenticity of COVID-19 information on social media?”) was asked to identify the participants’ cross-verification behavior, with answer options “Yes” and “No.” Although the Likert approach is more accurate in capturing the variation and degree of behavioral change and cross-validation, the criterion here was the presence or absence of respondents’ actual action; thus, binary measurement was used for analysis.
Descriptive statistics were used to assess all sociodemographic characteristics of the participants. Frequency and case-weighted percentages were calculated to describe sociodemographic parameters and level distributions among participants. Differences in characteristics between groups were investigated with descriptive analyses performed according to the characteristics of the data, including the chi-square test and Kruskal-Wallis test.
Multiple stepwise regression was used to examine the association between the independent and dependent variables [
A total of 15,055 Chinese netizens completed the survey, and 14,509 responses were included in the study after incomplete survey responses were excluded (14,509/15,055, 96.4%). The descriptive analysis shown in
Demographic characteristics of the participants.
Characteristic | Total (N=14,509), n (%) | Age groups (years) | ||||||||||||
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18-29 (N=5723), n (%) | 30-39 (N=6151), n (%) | 40-49 (N=1714), n (%) | ≥50 (N=921), n (%) |
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<.001a | |||||||||||||
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Male | 3008 (20.7) | 1297 (22.7) | 1139 (18.5) | 349 (20.4) | 223 (24.2) |
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Female | 11,501 (79.3) | 4426 (77.3) | 5012 (81.5) | 1365 (79.6) | 698 (75.8) |
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<.001b | |||||||||||||
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Junior high school or below | 407 (2.8) | 100 (1.7) | 89 (1.4) | 118 (6.9) | 100 (10.9) |
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High school | 1242 (8.6) | 368 (6.4) | 420 (6.8) | 240 (14.0) | 214 (23.2) |
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Junior college | 3068 (21.1) | 1218 (21.3) | 1115 (18.1) | 439 (25.6) | 296 (32.1) |
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Undergraduate degree | 7685 (53.0) | 3182 (55.6) | 3480 (56.6) | 742 (43.3) | 281 (30.5) |
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Master’s degree or above | 2107 (14.5) | 855 (14.9) | 1047 (17.0) | 175 (10.2) | 30 (3.3) |
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<.001a | |||||||||||||
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Student | 1661 (11.4) | 1637 (28.6) | 22 (0.4) | 1 (0.1) | 1 (0.1) |
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Staff member in the government | 2436 (16.8) | 656 (11.5) | 1282 (20.8) | 367 (21.4) | 131 (14.2) |
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Health care provider | 2192 (15.1) | 1075 (18.8) | 879 (14.3) | 183 (10.7) | 55 (6.0) |
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Staff member in a company | 3258 (22.5) | 978 (17.1) | 1737 (28.2) | 463 (27.0) | 80 (8.7) |
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Self-employed entrepreneur | 965 (6.7) | 270 (4.7) | 518 (8.4) | 142 (8.3) | 35 (3.8) |
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Other | 3997 (27.5) | 1107 (19.3) | 1713 (27.8) | 558 (32.6) | 619 (67.2) |
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<.001a | |||||||||||||
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First-tier city | 549 (3.8) | 280 (4.9) | 202 (3.3) | 47 (2.7) | 20 (2.2) |
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Second-tier city | 9133 (62.9) | 3562 (62.2) | 4078 (66.3) | 980 (57.2) | 513 (55.7) |
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Other city | 3978 (27.4) | 1444 (25.2) | 1646 (26.8) | 564 (32.9) | 324 (35.2) |
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Rural area | 849 (5.9) | 437 (7.6) | 225 (3.7) | 123 (7.2) | 64 (6.9) |
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<.001b | |||||||||||||
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Good | 9251 (63.8) | 4106 (71.7) | 3679 (59.8) | 962 (56.1) | 504 (54.7) |
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Medium | 4515 (31.1) | 1393 (24.3) | 2153 (35.0) | 643 (37.5) | 326 (35.4) |
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Poor | 743 (5.1) | 224 (3.9) | 319 (5.2) | 109 (6.4) | 91 (9.9) |
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<.001b | |||||||||||||
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High | 5978 (41.2) | 2589 (45.2) | 2373 (38.6) | 666 (38.9) | 350 (38.0) |
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Medium | 7090 (48.9) | 2598 (45.4) | 3155 (51.3) | 871 (50.8) | 466 (50.6) |
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Low | 1441 (9.9) | 536 (9.4) | 623 (10.1) | 177 (10.3) | 105 (11.4) |
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<.001b | |||||||||||||
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≤1 | 797 (5.5) | 266 (4.6) | 327 (5.3) | 119 (6.9) | 85 (9.2) |
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>1 to ≤3 | 7108 (49.0) | 2435 (42.5) | 3233 (52.6) | 925 (54.0) | 515 (55.9) |
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>3 to ≤5 | 4376 (30.2) | 1916 (33.5) | 1737 (28.2) | 485 (28.3) | 238 (25.8) |
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>5 to ≤7 | 1418 (9.8) | 670 (11.7) | 565 (9.2) | 122 (7.1) | 61 (6.6) |
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>7 | 810 (5.6) | 436 (7.6) | 289 (4.7) | 63 (3.7) | 22 (2.4) |
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<.001b | |||||||||||||
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Rarely | 573 (3.9) | 267 (4.7) | 230 (3.7) | 47 (2.7) | 29 (3.1) |
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Sometimes | 2107 (14.5) | 922 (16.1) | 912 (14.8) | 177 (10.3) | 96 (10.4) |
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Often | 11,829 (81.5) | 4534 (79.2) | 5009 (81.4) | 1490 (86.9) | 796 (86.4) |
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aChi-square test.
bKruskal-Wallis test.
Sources of COVID-19 information on social media and source trust scores.
Variable | Total (N=14,509) | Age groups (years) | |||||||||||||
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18-29 (N=5723) | 30-39 (N=6151) | 40-49 (N=1714) | ≥50 (N=921) |
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.04a | ||||||||
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Yes | 12,255 (84.5) | 4773 (83.4) | 5241 (85.2) | 1460 (85.2) | 781 (84.8) |
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No | 2254 (15.5) | 950 (16.6) | 910 (14.8) | 254 (14.8) | 140 (15.2) |
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<.001a | ||||||||
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Yes | 12,706 (87.6) | 5024 (87.8) | 5432 (88.3) | 1483 (86.5) | 767 (83.3) |
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No | 1803 (12.4) | 699 (12.2) | 719 (11.7) | 231 (13.5) | 154 (16.7) |
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<.001a | ||||||||
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Yes | 8124 (56.0) | 3570 (62.4) | 3357 (54.6) | 812 (47.4) | 385 (41.8) |
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No | 6385 (44.0) | 2153 (37.6) | 2794 (45.4) | 902 (52.6) | 536 (58.2) |
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<.001a | ||||||||
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Yes | 7107 (49.0) | 2743 (47.9) | 2964 (48.2) | 911 (53.2) | 489 (53.1) |
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No | 7402 (51.0) | 2980 (52.1) | 3187 (51.8) | 803 (46.8) | 432 (46.9) |
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<.001a | ||||||||
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Yes | 4017 (27.7) | 1671 (29.2) | 1595 (25.9) | 447 (26.1) | 304 (33.0) |
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No | 10,492 (72.3) | 4052 (70.8) | 4556 (74.1) | 1267 (73.9) | 617 (67.0) |
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Government agenciesb | 4.46 (0.76) | 4.49 (0.75) | 4.46 (0.76) | 4.39 (0.77) | 4.40 (0.82) | <.001c | ||||||||
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Professional news mediad | 4.18 (0.79) | 4.18 (0.80) | 4.21 (0.77) | 4.15 (0.79) | 4.11 (0.87) | .002c | ||||||||
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Health care mediae | 3.86 (0.87) | 3.87 (0.88) | 3.89 (0.85) | 3.80 (0.86) | 3.78 (0.89) | <.001c | ||||||||
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Hospital institutionsf | 4.52 (0.69) | 4.53 (0.67) | 4.53 (0.68) | 4.50 (0.71) | 4.51 (0.74) | .76c | ||||||||
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Celebritiesg | 3.21 (1.07) | 3.27 (1.07) | 3.18 (1.04) | 3.15 (1.10) | 3.14 (1.13) | <.001c |
aChi-square test.
bGovernment agencies, such as the Chinese State Council, which often serve as the voice of official or administrative institutions.
cKruskal-Wallis test.
dProfessional news media outlets, such as Sina Release, which focus on instant news reporting in the professional domain.
eHealth care institutions, such as the US Centers for Disease Control and Prevention, which often cover trends in the medical field and issue public health advisories.
fHospital institutions, such as West China Hospital accounts, which disseminate prevention and treatment information.
gCelebrities who have a large number of social media followers and overall social and consumer influence [
Participants’ knowledge about COVID-19.
Questions and responses | Value (N=14,509), n (%) | |
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Droplet (correct option) | 14,214 (98.0) |
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Airborne (correct option) | 8990 (62.0) |
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Close contact (correct option) | 12,353 (85.1) |
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Cloth mask (correct option) | 13,786 (95.0) |
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Disposable medical mask | 254 (1.8) |
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Medical-surgical mask | 292 (2.0) |
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N95 protective mask | 177 (1.2) |
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If conditions permit, populations in dense areas should change their disposable masks around 4 hours | 522 (3.6) |
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Once contaminated, it should be replaced as soon as possible | 291 (2.0) |
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Avoid touching the inner face of the mask with your hands | 239 (1.6) |
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Cotton masks resist the coronavirus better than medical masks (correct option) | 13,457 (92.8) |
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Rinsing with light saltwater | 148 (1.0) |
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Sauna or steaming | 102 (0.7) |
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Drinking alcohol | 198 (1.4) |
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Wearing masks (correct option) | 14,061 (96.9) |
aThere were multiple correct options.
We identified the following groups as having a higher likelihood of obtaining accurate COVID-19 preventive information: female participants (
Among 4017 participants who searched for COVID-19 information on celebrities’ social media accounts, those who were female, were aged ≥50 years, were non–health care workers, had a higher perceived health condition and health literacy, and had a higher frequency of searching had greater odds of behavioral changes based on COVID-19 web-based information (
In terms of subgroups who searched for COVID-19 web-based information released by celebrities and who were more likely to change their health behaviors, we found that being female (aOR 0.767, 95% CI 0.544-0.928;
Multiple linear regression results of the association of COVID-19 knowledge with demographic characteristics and social media use.
Variable | COVID-19 knowledge score | ||||||||||
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Coefficient | Standard error | |||||||||
Gender (female vs male) | 0.172 | 0.018 | <.001 | ||||||||
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30-39 | 0.075 | 0.017 | <.001 | |||||||
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40-49 | 0.108 | 0.025 | <.001 | |||||||
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≥50 | −0.138 | 0.032 | <.001 | |||||||
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High school | 0.050 | 0.049 | .30 | |||||||
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Junior college | 0.052 | 0.046 | .26 | |||||||
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Undergraduate degree | 0048 | 0.046 | .30 | |||||||
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Master’s degree or above | −0.016 | 0.049 | .75 | |||||||
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Staff member in the government | −0.003 | 0.030 | .91 | |||||||
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Health care provider | 0.073 | 0.029 | .01 | |||||||
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Staff member in a company | 0.006 | 0.029 | .84 | |||||||
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Self-employed entrepreneur | 0.002 | 0.037 | .95 | |||||||
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Other | 0.053 | 0.028 | .06 | |||||||
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First-tier city | 0.158 | 0.037 | <.001 | |||||||
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Second-tier city | 0.160 | 0.039 | <.001 | |||||||
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Other city | 0.168 | 0.047 | <.001 | |||||||
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Medium | 0.001 | 0.016 | .96 | |||||||
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Poor | 0.131 | 0.033 | <.001 | |||||||
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Medium | 0.019 | 0.016 | .22 | |||||||
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Low | −0.026 | 0.027 | .33 | |||||||
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>1 to ≤3 | 0.111 | 0.032 | .001 | |||||||
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>3 to ≤5 | 0.093 | 0.033 | .005 | |||||||
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>5 to ≤7 | 0.060 | 0.038 | .11 | |||||||
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>7 | 0.101 | 0.043 | .02 | |||||||
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Sometimes | 0.305 | 0.040 | <.001 | |||||||
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Often | 0.379 | 0.037 | <.001 | |||||||
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Government agencies (no vs yes) | 0.188 | 0.020 | <.001 | |||||||
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Professional news media (no vs yes) | 0.245 | 0.022 | <.001 | |||||||
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Health care media (no vs yes) | 0.063 | 0.015 | <.001 | |||||||
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Hospital institutions (no vs yes) | 0.094 | 0.015 | <.001 | |||||||
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Celebrities (no vs yes) | 0.087 | 0.017 | <.001 | |||||||
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2 | —a | — | N/Ab | ||||||
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3 | — | — | N/A | ||||||
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4 | — | — | N/A | ||||||
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5 | — | — | N/A | ||||||
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2 | 0.064 | 0.123 | .60 | ||||||
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3 | 0.384 | 0.116 | .001 | ||||||
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4 | 0.364 | 0.115 | .002 | ||||||
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5 | 0.421 | 0.116 | <.001 | ||||||
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2 | −0.037 | 0.085 | .67 | ||||||
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3 | −0.127 | 0.080 | .11 | ||||||
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4 | −0.135 | 0.080 | .09 | ||||||
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5 | −0.183 | 0.081 | .06 | ||||||
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2 | −0.163 | 0.143 | .25 | ||||||
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3 | 0.256 | 0.122 | .04 | ||||||
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4 | 0.376 | 0.119 | .002 | ||||||
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5 | 0.444 | 0.119 | <.001 | ||||||
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2 | 0.000 | 0.032 | .99 | ||||||
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3 | 0.025 | 0.029 | .39 | ||||||
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4 | −0.045 | 0.030 | .14 | ||||||
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5 | −0.120 | 0.035 | .001 |
aThe corresponding variable has not been included in the final multiple regression model.
bN/A: not applicable.
As shown in
Multiple logistic regression results of the association between behavior change and verification.
Variable | Behavior change | Information verification (among netizens searching web-based COVID-19 information released by celebrities) | |||||||||||||||
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Change vs no change | Behavior change group | No behavior change group | ||||||||||||||
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aORa (95% CI) | Verify vs not verify, aOR (95% CI) | Verify vs not verify, aOR (95% CI) |
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COVID-19 knowledge score | 1.085 (1.036-1.191) | .045 | —b | N/Ac | — | N/A |
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Gender (female vs male) | 1.301 (1.085-1.556) | .004 | 0.767 (0.544-0.928) | <.001 | 1.419 (1.050-1.921) | .02 |
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30-39 | 1.161 (0.981-1.374) | .08 | — | N/A | — | N/A |
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40-49 | 1.284 (0.998-1.660) | .054 | — | N/A | — | N/A |
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≥50 | 1.519 (1.116-2.089) | .009 | — | N/A | — | N/A |
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High school | — | N/A | 0.695 (0.386-1.233) | .22 | — | N/A |
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Junior college | — | N/A | 0.786 (0.452-1.345) | .39 | — | N/A |
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Undergraduate degree | — | N/A | 0.613 (0.357-1.034) | .07 | — | N/A |
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Master’s degree or above | — | N/A | 0.725 (0.409-1.268) | .27 | — | N/A |
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Staff member in the government | 1.053 (0.779-1.425) | .74 | — | N/A | — | N/A |
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Health care provider | 0.721 (0.550-0.943) | .02 | — | N/A | — | N/A |
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Staff member in a company | 1.130 (0.850-1.499) | .40 | — | N/A | — | N/A |
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Self-employed entrepreneur | 1.140 (0.797-1.639) | .48 | — | N/A | — | N/A |
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Other | 1.045 (0.792-1.378) | .75 | — | N/A | — | N/A |
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First-tier city | — | N/A | 1.455 (1.260-2.144) | <.001 | — | N/A |
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Second-tier city | — | N/A | 1.281 (0.899-1.419) | .06 | — | N/A |
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Other city | — | N/A | 0.799 (0.526-1.200) | .28 | — | N/A |
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Medium | 1.046 (0.893-1.226) | .58 | 0.789 (0.664-0.939) | .007 | — | N/A |
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Poor | 0.578 (0.419-0.801) | <.001 | 0.770 (0.509-1.167) | .22 | — | N/A |
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Medium | 0.718 (0.454-0.956) | <.001 | 0.596 (0.505-0.703) | <.001 | 0.614 (0.476-0.791) | <.001 |
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Low | 0.845 (0.570-0.989) | .03 | 0.441 (0.323-0.600) | <.001 | 0.529 (0.338-0.822) | .005 |
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>1 to ≤3 | — | N/A | 1.156 (0.741-1.790) | .52 | — | N/A |
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>3 to ≤5 | — | N/A | 0.809 (0.514-1.262) | .35 | — | N/A |
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|
>5 to ≤7 | — | N/A | 1.258 (0.770-2.044) | .36 | — | N/A |
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|
>7 | — | N/A | 1.009 (0.602-1.683) | .97 | — | N/A |
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|
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|
Sometimes | 1.379 (0.827-2.295) | <.001 | 1.077 (0.458-1.786) | .92 | 1.545 (0.675-3.885) | .33 |
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|
Often | 2.477 (1.541-3.974) | <.001 | 3.239 (1.632-6.788) | <.001 | 4.077 (1.906-9.742) | .001 |
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2 | 1.043 (0.668-1.617) | .85 | 0.803 (0.681-0.939) | .04 | — | N/A |
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3 | 1.330 (0.889-1.972) | .16 | 0.518 (0.374-0.777) | <.001 | — | N/A |
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4 | 1.771 (1.182-2.629) | .005 | 0.625 (0.322-0.909) | <.001 | — | N/A |
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|
5 | 2.497 (1.630-3.794) | <.001 | 0.386 (0.107-0.519) | <.001 | — | N/A |
|
aaOR: adjusted odds ratio.
bThe corresponding variable has not been included in the final multiple regression model.
cN/A: not applicable.
Overall, with the advancement of smart device technology, the use of the internet has penetrated various age groups. More than 90% of those investigated reported surfing social media for more than 1 hour per day, including middle-aged and older participants. The findings also showed that social media use and the credibility of web-based information among different age groups varied. The age gap should be considered as much as possible in broadening the diffusion of preventive measures for COVID-19 via social media platforms. The results also indicated that “frequency” had a more significant impact on COVID-19 literacy than the length of time spent using social media. In other words, “how often” individuals consulted social media directly, rather than “how long,” had a strong relationship with preventive behaviors [
Similar to a prior study that found that women had higher COVID-19 literacy [
Differing levels of trust in 5 web-based information sources on social media were found to be another significant predictor of preventive behaviors. For web-based information released by professional media and hospital institutions, higher trust was associated with a positive relationship with COVID-19 literacy. However, Chinese netizens with high trust in web-based information released by celebrities seemed to have less COVID-19 knowledge. The results indicate that the accuracy of COVID-19 information from individual and unofficial social media accounts, including those of movie stars and singers, deserves more attention than official social media accounts in terms of the effect on preventive measures, particularly for Chinese netizens. Celebrities were more influential in disseminating the related information via social media platforms, especially among young Chinese netizens [
The results showed that women were more likely than men to change their health care behaviors according to web-based information released by unofficial accounts. Women may be more sentimental and sensitive, may experience more severe stress and anxiety during the pandemic [
Additionally, social media use frequency had a significant relationship with Chinese netizens’ adoption of web-based health care advice and changes to their preventive behaviors. Thus, “frequency” may be a more significant predictor of social media effects. Social media use frequency should therefore be an effective strategy for public health promotion, especially when countries are confronted with COVID-19 vaccination hesitancy [
The relationship between health literacy and health behaviors has been widely recognized [
Although social media–based information may help specific groups improve their ability to deal with the pandemic, individuals may also take risks in their use of web-based resources, because web-based information released by individual accounts is not always accurate [
As previous research has illustrated, health literacy has been underestimated, and more emphasis should be placed on it during the pandemic [
Furthermore, the high-frequency use of social media and search for information, rather than the time spent on social media, fostered the ability of groups to cross-verify information. This phenomenon was more commonly associated with the current practices of social media companies using algorithms that repeatedly drive similar content to users based on what they have recently browsed [
The importance of cross-referencing was heavily based upon the likely veracity of the information obtained. In this context, web-based information released from official social media accounts, such as the government, the Centers for Disease Control and Prevention (CDC), and hospital institutions, is likely highly accurate, while that from individual social media accounts may be inaccurate and thus even more in need of cross-verification with official sources. Notably, in our research, Chinese netizens who trusted web-based COVID-19 information released by celebrities usually conducted little cross-verification of web-based information before changing their behaviors. Even worse, this survey found that netizens who highly trusted web-based information posted by celebrities were less knowledgeable of COVID-19 preventive measures and more likely to change their health care behaviors based on that online information. According to a Twitter survey, during the pandemic, the tweets of celebrities and politicians related to COVID-19 outperformed those of health and scientific institutions [
The pandemic is accompanied by an infodemic that involves the abundant and uncontrolled spread of potentially harmful misinformation, mainly produced by unofficial social media accounts [
This study has several strengths. First, the sample was relatively large and widely representative, which provided the opportunity for accurate examination of potential variations. Moreover, this study extends the current literature on the characteristics of Chinese netizens who are likely to change their health behaviors according to unofficial web-based information, but seldom conduct cross-verification. As countries across the world continue to battle the pandemic and confront increased use of social media for health information dissemination, similar research in the infodemic management field is expected.
Despite its strengths, several limitations of our study should be acknowledged. First, it included only 3 WCH social media platforms and people who had access to the internet and electronic devices, thereby excluding people who did not. Additionally, since this was a cross-sectional study conducted between May 18 and May 31, 2020, the results may not be generalizable and thus may fail to capture changes over time due to rapid social development. The online survey had very low response rates among older people. Considering the low use of the internet among older groups, further studies should focus on the use of traditional media for older people during the pandemic. Moreover, the self-designed questionnaire failed to evaluate the actual age, obtain a more detailed educational degree, and use a 1-5 scale of medical knowledge, which would have allowed for the collection of more specific information from the respondents. In addition, the internal validity may be an issue because WCH social media followers were encouraged to distribute the questionnaire to their relatives and friends who met the inclusion criteria. However, considering that the questionnaire items did not involve any individual interests and emphasized voluntary and uncompelled survey participation, unintentional bias associated with participant relationships was a remote possibility. Moreover, the study was completely voluntary, so the characteristics of individuals who would actively choose to participate should be considered since self-reported health status and literacy levels are highly subjective. Similarly, the study could not accurately predict netizens’ health behaviors based on self-reported behavioral change and cross-verification. However, it provides a preliminary analysis and clarifies associations between various characteristics.
Additionally, the behavioral change tendencies included in this study are not necessarily positive or negative because the survey could not discern what information a change was in response to and whether it was an effective change. Moreover, information verification is difficult to measure and is detrimental only when the information is inaccurate. Therefore, further studies regarding verification strategies are necessary. Furthermore, the sample included many more individuals with high education levels and netizens from urban areas. Future studies should include netizens with less education and those who live in rural areas to facilitate the generalizability of the findings. Moreover, this cross-sectional study focused mainly on investigating phenomena, and the barriers, facilitators, and causal loops for behavioral change and cross-verification were not included. Further research is necessary to explore what motivates individuals’ social media use, as well as barriers to and facilitators of the validation of web-based information. Finally, with the increasing popularity of social media, people’s health literacy and eHealth literacy have been continuously improving over the last few years, and future research with a wider time span could be conducted to investigate changes in cross-verification behaviors.
In general, this study made the first attempt to examine whether cross-verification was implemented before Chinese netizens engaged in changes related to health behavior–based information on unofficial social media. The study found that Chinese netizens who were female, lived in rural areas, had less health literacy, searched less frequently for online information, and had high trust in web-based information released by celebrities were more likely to be misled by misinformation on social media, since they were more likely to easily change their health behaviors without fact-checking and cross-verifying web-based information. These findings have practical implications for the government, health organizations, and health practitioners in designing and implementing health promotions and interventions in similar pandemics. Netizens with the aforementioned characteristics should be informed about the risk of misinformation and the strategies for verifying the accuracy of web-based COVID-19 information to protect them from using counterfeit, inappropriate, or unsafe preventive measures. More technical and policy efforts are needed to further address the dissemination of misinformation on social media.
Survey questionnaire.
artificial intelligence
adjusted odds ratio
content validity index
West China Hospital
This work was supported by the National Natural Science Foundation of China (grant number 71874115) and the special funds for COVID-19 Prevention and Control of West China Hospital of Sichuan University (HX-2019-nCoV-023). The funder had no role in the design of the study; the collection, analysis, and interpretation of the data; or the writing of the manuscript. We sincerely thank Prof Jalali for insightful guidance and valuable discussion. The first author would like to express her gratitude to Mr Guanhua Qing for his continuous support and encouragement during this research.
PL conducted the survey and statistical analysis, and drafted the manuscript. BC designed the study and questionnaire. GD consulted on the analysis and interpretation of the results, contributed to further development of the analysis and content, and revised the manuscript for important intellectual content. YL and WT helped to perform the statistical analysis and interpret the data. WL contributed to the manuscript revision. JW and YZ were the principal designers of the study and were responsible for all the results of the study, as well as the review and approval of the manuscript. All authors read and approved the final manuscript.
None declared.