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Social media have become the source of choice for many users to search for health information on COVID-19 despite possible detrimental consequences. Several studies have analyzed the association between health information–searching behavior and mental health. Some of these studies examined users’ intentions in searching health information on social media and the impact of social media use on mental health in Indonesia.
This study investigates both active and passive participation in social media, shedding light on cofounding effects from these different forms of engagement. In addition, this study analyses the role of trust in social media platforms and its effect on public health outcomes. Thus, the purpose of this study is to analyze the impact of social media usage on COVID-19 protective behavior in Indonesia. The most commonly used social media platforms are Instagram, Facebook, YouTube, TikTok, and Twitter.
We used primary data from an online survey. We processed 414 answers to a structured questionnaire to evaluate the relationship between these users’ active and passive participation in social media, trust in social media, anxiety, self-efficacy, and protective behavior to COVID-19. We modeled the data using partial least square structural equation modeling.
This study reveals that social media trust is a crucial antecedent, where trust in social media is positively associated with active contribution and passive consumption of COVID-19 content in social media, users’ anxiety, self-efficacy, and protective behavior. This study found that active contribution of content related to COVID-19 on social media is positively correlated with anxiety, while passive participation increases self-efficacy and, in turn, protective behavior. This study also found that active participation is associated with negative health outcomes, while passive participation has the opposite effects. The results of this study can potentially be used for other infectious diseases, for example, dengue fever and diseases that can be transmitted through the air and have handling protocols similar to that of COVID-19.
Public health campaigns can use social media for health promotion. Public health campaigns should post positive messages and distil the received information parsimoniously to avoid unnecessary and possibly counterproductive increased anxiety of the users.
Since their inception a decade or so ago, social media platforms have steadily increased their prevalence as substitutes for face-to-face interactions and other existing forms of communication. From the start of the pandemic social media have become the main virtual agora where news and opinions about COVID-19 have been posted, shared, and commented on by public and private organizations and by individuals [
This study presents an analysis of the effects of social media usage on adaptive responses to epidemics. In times of uncertainty, users contribute or consume social media content, which could act as a catalyst for anxiety, in turn triggering protective action [
The COVID-19 situation as a pandemic was first described on March 11, 2020, and in early 2020, the first 2 cases of COVID-19 in Indonesia had been confirmed [
From the second half of 2021 to the closure of our study, Indonesia ranked worldwide in the top quantile of the Stringency Index, a composite of containment, closure, and health system policy indicators comparable across countries [
Kemp [
Previous research on health information in social media is limited to the analysis of secondary data, especially social media content with a specific theme for certain disease/health information, and did not focus on COVID-19–related information. Tsao et al [
Moreover, Wang et al [
The ability of social media to influence human behavior has promoted them to the role of media of choice for modern marketing [
Negative emotions, such as worry and fear, seem to be powerful nudges [
Fear can be particularly effective when paired with high levels of self-efficacy, that is, the belief in one’s ability to execute certain behaviors to achieve the desired goal. Fear-inducing messages that carry high efficacy, for example, “COVID-19 is highly contagious, but can be avoided by wearing a face mask,” tend to promote protective behavior compared with those with low efficacy, which tend to be rejected by the receiver [
Studies using convenient samples have also found that fear and self-efficacy mediate the path from media usage to protective behavior. Zhang et al [
In contrast to the previous 3 studies, Yoo et al [
Given the findings observed in the aforementioned studies, we would like to confirm whether a similar hypothesis also holds in our empirical setting, namely:
H1: Active contribution of COVID-19 content in social media will be positively associated with anxiety (H1a) and self-efficacy (H1b) to COVID-19.
H2: Active contribution of COVID-19 content in social media will be positively associated with protective behavior against COVID-19.
H3: Passive consumption of COVID-19 social media content will be positively associated with anxiety (H3a) and self-efficacy (H3b) to COVID-19.
H4: Passive consumption of COVID-19 social media content will be positively associated with protective behavior against COVID-19.
H5: Anxiety (H5a) and self-efficacy (H5b) to COVID-19 will be positively associated with protective behavior against COVID-19.
Gibson and Trnka [
Therefore, trust plays a crucial role in social media; thus, this variable must be studied by academics [
Compared with traditional media, social media allow the fast spread of information and the publication of less trustworthy materials in which both the veracity of the message and the intention of the publisher are challenging to verify. Social media platforms are also responsible for mediating the content delivered to their users using automated mechanisms not open to scrutiny. Increased social media activity can contribute to the fast spread of false information [
We thus consider the following hypothesis:
H6: Trust on social media is positively associated with the active contribution of COVID-19 content in social media (H6a), and the passive consumption of COVID-19 social media content (H6b).
H7: Trust on social media is positively associated with anxiety (H7a) and self-efficacy (H7b) to COVID-19.
H8: Trust on social media positively affects protective behavior against COVID-19.
We can express these relationships as a direct acyclic graph, depicted in
Hypothesized relationship between constructs.
We propose and define 6 constructs in our proposed model: active participation, passive participation, anxiety, self-efficacy, protective behavior, and trust.
First and second, we propose “active participation” and “passive participation” to qualify the engagement of social media users, respectively. The former refers to the publication of COVID-19 content on social media, whereas the latter to its reception and consumption. These constructs follow the discussion in Chen et al [
Third, we consider “anxiety,” defined as the feeling of tension, worried thoughts, and physical changes aroused when reading COVID-19 materials on social media.
Fourth, we define self-efficacy as an individual’s belief in his/her ability to protect herself/himself against COVID-19 [
Fifth, modifying Yoo et al [
Finally, we define trust as a firm belief in the integrity of social media platforms and their content.
The 6 constructs were measured in terms of reflective indicators as captured in responses to questions and are listed in
All responses were collected on a Likert scale. Responses to questions meant to quantify active participation and passive participation used a Likert scale with the following subjective frequency levels: 5=very frequently, 4=frequently, 3=sometimes, 2=rarely, and 1=never. Questions quantifying the 4 other constructs used Likert scales with the following agreement levels: 5=strongly agree, 4=agree, 3=neutral, 2=disagree, and 1=strongly disagree.
We then used structural equation modeling to test the hypotheses [
We used an online survey approach driven by the COVID-19 pandemic, as this is expected to reach wider respondents. The questionnaire was prepared in Indonesian language with the expectation that the respondents will easily understand the questions. Prior to the survey distribution, we carried out a pilot study involving 30 respondents who have actively used social media. The Cronbach α value for each variable was above .7. Moreover, we conducted an online survey from February 28, 2022, to March 28 2022. The respondents were all Indonesian residents. The survey instrument was prepared in Indonesian and contained 50 questions organized into 4 sections corresponding to demographics, frequency of social media participation, perceptions of COVID-19 social media content, and additional details on social media behavior. Links to the survey were distributed through popular social media platforms in Indonesia, such as Instagram, Facebook, Twitter, WhatsApp, Telegram, and Line (NHN Japan [now Line Corporation]). Respondents were encouraged to share the links to the survey with their social networks; thus, we used snowball sampling to make it easier to find trusted respondents from the network of friends that each respondent has. The age of users ranged from 18 to 34 years, and comprised approximately 65% (~110 million) of the total active users of social media [
This study has received approval from the Faculty of Computer Science, University of Indonesia and all respondents have agreed to participate in this study. No ethics board review was sought as all respondent data are anonymous and the data can only be used for the purposes of this research.
The survey responses were evenly distributed across gender. There was a 2-peaked income distribution with mass at both extremes (<IDR 3 million and >IDR 7 million; IDR 1=US $0.000066); 7 out of 10 respondents resided in Jakarta (the capital city of Indonesia). More than one-half of the respondents were under the age of 30 and undergraduate students. According to Kemp [
Respondents demographics (N=414).
Demographics | Values, n (%) | |||
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Male | 204 (49.3) | |
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Female | 210 (50.7) | |
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<3 | 161 (38.9) | |
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3-5 | 51 (12.3) | |
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5-7 | 48 (11.6) | |
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>7 | 154 (37.2) | |
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Jakarta | 279 (67.4) | |
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Java (excluding Jakarta) | 64 (15.5) | |
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Sumatera | 20 (4.8) | |
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Kalimantan | 19 (4.6) | |
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Others | 32 (7.7) | |
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<17 | 4 (1.0) | |
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17-27 | 218 (52.7) | |
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28-38 | 115 (27.8) | |
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39-49 | 60 (14.5) | |
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>50 | 17 (4.1) | |
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347 (83.8) | ||
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146 (35.3) | ||
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YouTube | 302 (72.9) | |
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TikTok | 112 (27.1) | |
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166 (40.1) | ||
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Government | 137 (33.1) | |
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News organizations | 283 (68.4) | |
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Private organizations | 115 (27.8) | |
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Influencers | 279 (67.4) | |
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Health statistics | 162 (39. 1) | |
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Users’ comments | 212 (51.2) | |
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Health information | 287 (69.3) |
aIDR 1=US $0.000066.
The number of items k used for each construct and their internal consistency as measured by Dijkstra and Henseler ρA are displayed in
Hypotheses H1-H8 are represented in terms of the direct acyclic graph (
Hypothesis testing resultsa.
Results | Reliability | Regressionsb | |||||||
k | ρA | AVEc | Active participation | Passive participation | Anxiety | Self-efficacy | Protective behavior | ||
Active participation | 4 | 0.917 | 0.770 | N/Ad | N/A | 0.245e; 0.058; |
0.034; 0.045; |
–0.210e; 0.062; |
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Passive participation | 3 | 0.767 | 0.677 | N/A | N/A | –0.023; 0.053; |
0.270e; 0.051; |
0.085; 0.060; |
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Anxiety | 7 | 0.937 | 0.689 | N/A | N/A | N/A | N/A | –0.064; 0.044; |
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Self-efficacy | 4 | 0.879 | 0.721 | N/A | N/A | N/A | N/A | 0.207e; 0.053; |
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Trust | 4 | 0.905 | 0.781 | .272e; 0.048; |
.413e; 0.039; |
0.123f; 0.054; |
0.359e; 0.053; |
0.246e; 0.053; |
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Protective behavior | 6 | 0.867 | 0.581 | N/A | N/A | N/A | N/A | N/A | |
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0.074 | 0.170 | 0.085 | 0.296 | 0.170 | |
Akaike information criterion |
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–28.8 | –74.4 | –29.8 | –138.6 | –66.3 | |
Total number of observations (N) |
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414 | 414 | 414 | 414 | 414 |
aThe table reports reliability tests on the first 3 columns: k is the number of items in each construct; Dijkstra and Henseler ρA is a measure of internal consistency; AVE is a measure of convergence validity. The last 5 columns display the coefficients of the fitted linear regressions for the model displayed in
bAfter each regression coefficient, the bootstrapped SEs are reported, followed by the P values and the corresponding hypothesis. The hypotheses that are validated by our data are marked with ✓.
cAVE: average variance extracted.
dN/A: not applicable.
e
f
Result of heterotrait-monotrait ratio.
Measure | Active participation | Passive participation | Anxiety | Self-efficacy | Protective behavior |
Passive participation | 0.49 | N/Aa | N/A | N/A | N/A |
Anxiety | 0.28 | 0.15 | N/A | N/A | N/A |
Self-efficacy | 0.27 | 0.52 | 0.27 | N/A | N/A |
Protective behavior | 0.12 | 0.23 | 0.09 | 0.34 | N/A |
Trust | 0.30 | 0.49 | 0.20 | 0.54 | 0.35 |
aN/A: not applicable.
The average variance extracted (AVE) for all fitted constructs is above the recommended level of 0.5 [
Concerning the fitted coefficients, we observed an agreement with hypothesis H1a, which posits a positive relationship between passive social media participation and anxiety. By contrast, hypotheses H1b (
Moreover, we wanted to uncover the roles that trust in social media could play on the other variables. Indeed, we found that trust is positively and significantly associated with all of our constructs in agreement with hypotheses H6a (
As we observed a lack of relationship between the constructs “active participation” and “self-efficacy,” we fitted an alternative model in which the causality between those variables is reversed, and there is a direct path between “passive participation” and “active participation.” In this alternative model, we found a statistically significant coefficient between “passive participation” and “active participation” equal to 0.354 (
With 6 constructs under investigation, there are just above 3 million possible networks. It is thus computationally feasible to evaluate the fit of all possible models defined as a direct acyclic graph. As a robustness check, we investigated whether our proposed model could capture most of the variation in the data as compared with alternatives based on the Akaike information criterion (AIC). Although seldom used for exploratory analysis, structural equation modeling is suited for this sort of exercise [
The AIC for our model is presented in
This study shows that passive and active participation in social media has diametrically opposite effects. On the one hand, the active contribution of COVID-19 content in social media is detrimental to public health outcomes because it is associated positively with anxiety, negatively with protective behavior, and does not affect self-efficacy. Our results align with Yoo et al [
On the other hand, this study finds that passive participation in social media was positively associated with public health outcomes. We identified a strong positive relationship between passive participation and self-efficacy, but no statistically significant relationship between anxiety and protective behavior. Appropriate practices toward COVID-19 are influenced by user’s good COVID-19 knowledge [
Finally, this study shows that social media trust is a crucial antecedent where trust in social media is positively associated with active contribution and passive consumption of COVID-19 content in social media, users’ anxiety, self-efficacy, and protective behavior. Plohl and Musil [
When trusted friends post messages concerning the efficacy or impact of various COVID-19 vaccines, personally connected people consider those messages with care. According to our study, the more trust users have in COVID-19 content on social media, the more protective behaviors they deploy. Therefore, social media trust indeed influences users’ compliance with COVID-19 protocol guidance [
This study analyzes the effects of social media usage on user’s protective behavior against the COVID-19 epidemic in Indonesia, the fourth most populous country in the world. This study also investigated both active and passive participation in social media, shedding light on cofounding effects of these different forms of engagement. Moreover, this study analyzed the role of trust in social media platforms and its effect on public health outcomes. Thus, this study enriches the study of Wijayanti et al [
This study provides practical implications for public health campaigns and social media users. Public health campaigns should use social media to post health promotion content to make social media users more aware of protecting themselves from infectious diseases. Social media users should also be aware of their active participation on social media with the hope of releasing their anxieties. Passive participation on social media was positively associated with public health outcomes. Therefore, social media users should consume health information from credible sources such as news, governments, or peers’ social media accounts. Social media providers should also filter their content to increase their users’ trust. Social media providers, together with public health campaigns, should provide awareness and knowledge about how to filter credible messages to social media users. Currently, the number of COVID-19–positive cases is starting to decrease; accordingly, the results of this study can potentially be used for other infectious diseases that have handling characteristics like COVID-19, for example, for dengue fever and others that can be transmitted by air and have handling protocols similar to COVID-19.
The respondents in this study are mostly located in the greater Jakarta region and may not faithfully reflect the diversity of the population of Indonesia. Then, although there likely exist feedback loops between some investigated constructs, we refrain from modeling such loops because of limitations in structural equation modeling. For instance, higher levels of active participation may lead to higher levels of passive social media participation because those users that post content are likely to spend more time reading reactions to their material. The challenge with feedback loops is that they can be hard to identify statistically. Dijkstra and Henseler [
Opposite effects for active and passive participation in social media are found in this study. When social media is used passively, much like traditional media, we observe more positive public health outcomes. By contrast, active participation is associated with worse health outcomes. Thus, social media could be used as a medium for health promotion. However, public health campaigns must be aware of this reality when engaging with social media users. Future studies should further investigate social media interventions to make social media users more active in posting health-related information on social media. Moreover, they can analyze the tendency from social media consumption that could have an impact on social media users’ anxiety and fear.
Questionnaire.
Akaike information criterion
average variance extracted
This research is supported by an internal publication grant from the Faculty of Computer Science, Universitas Indonesia.
The data sets during generated or analyzed during this study are not publicly available due to the lack of authority to share data.
None declared.