Source Trust and COVID-19 Information Sharing: The Mediating Roles of Emotions and Beliefs About Sharing

Abstract

Health information sharing has become especially important during the COVID-19 (coronavirus disease 2019) pandemic because people need to learn about the disease and then act accordingly. This study examines the perceived trust of different COVID-19 information sources (health professionals, academic institutions, government agencies, news media, social media, family, and friends) and sharing of COVID-19 information in China. Specifically, it investigates how beliefs about sharing and emotions mediate the effects of perceived source trust on source-specific information sharing intentions. Results suggest that health professionals, academic institutions, and government agencies are trusted sources of information and that people share information from these sources because they think doing so will increase disease awareness and promote disease prevention. People may also choose to share COVID-19 information from news media, social media, and family as they cope with anxiety, anger, and fear. Taken together, a better understanding of the distinct psychological mechanisms underlying health information sharing from different sources can help contribute to more effective sharing of information about COVID-19 prevention and to manage negative emotion contagion during the pandemic.

Keywords

behavioral beliefs COVID-19 emotions health information sharing negativity bias

Coronavirus disease 2019 (COVID-19) refers to the acute respiratory disease that is caused by the new coronavirus SARS-CoV-2 identified in 2019 (World Health Organization [WHO], 2020c). The virus that causes COVID-19 is highly infectious and can easily transmit between people. Preventive measures include wearing facial masks, washing hands, and social distancing (WHO, 2020b). In early 2020, human transmissions of coronavirus were confirmed around the world, causing a global health emergency (Nature, 2020). As of July 24, 2020, the COVID-19 pandemic had more than 15 million total confirmed cases worldwide (Johns Hopkins University Coronavirus Resource Center, n.d.).

Health information about COVID-19 is essential to help people prevent infection. Past studies on health information acquisition suggest that people either actively seek or inadvertently scan health information (Kelly et al., 2010; Niederdeppe et al., 2007). Information scanning emphasizes incidental exposure and represents the typical way of how average people learn about health conditions (Kelly et al., 2010). In cases of salient public health threats such as COVID-19, government agencies and media professionals who are responsible for providing updates on public health emergencies have lost their monopoly in disseminating information through traditional media and government/news websites. In the digital age, large-scale health information sharing by average people on social media and through interpersonal conversations also affects both the availability and accessibility of needed information. Although health information acquisition has been well investigated (Wigfall & Friedman, 2016; Zhao et al., 2015), less research attention has been paid to health information sharing.

People may share health-related information from a variety of interpersonal, organizational, and mediated communication sources (e.g., health professionals, government agencies, and social media; Dutta-Bergman, 2003; National Cancer Institute [NCI], 2019). Source trust as an important indicator of source credibility can affect the persuasive power of messages such that messages from highly trusted sources are more likely to evoke changes in attitudes and behaviors (Kumkale et al., 2010; Pornpitakpan, 2004). It is unclear whether perceived source trust also predicts intentions to share messages. Furthermore, the psychological mechanisms underlying the relationship between perceived source trust and source-specific sharing intentions also remain unknown. Thus, this study examines the relationship between perceived trust of COVID-19 information sources and people’s intentions to share COVID-19 messages from several information sources for Chinese people, along with its possible underlying psychological processes.

Perceived Source Trust and Health Information Sharing

There exists a wide range of sources for health information with varying degrees of perceived trustworthiness (NCI, 2019). Sources with high levels of perceived trustworthiness are expected to convey truthful, accurate information and are hence less likely to misguide readers (O’Keefe, 2015). Previous research examining general health information sources found that doctors and government agencies are the most trusted sources (Dutta-Bergman, 2003; NCI, 2019).

Source trust has been shown to influence message processing, issue attitudes, and behaviors such that messages from highly trusted sources tend to gain an edge on persuasion although such effects are conditional (Pornpitakpan, 2004). For example, when readers do not have strong prior attitudes or are unable to form attitudes due to a lack of knowledge on the issue, source trust exerts more persuasive influence (Kumkale et al., 2010).

COVID-19 poses new threats/risks to public health (WHO, 2020b, 2020c) highlighting the importance of examining perceived health information source trust: When people lack prior experience or knowledge about the disease, it heightens the effects of perceived source trust on the persuasiveness of COVID-19 messages in shaping readers’ attitudes and behaviors.

Along this line, because source trust can increase the likelihood of people acting according to the information they received from that source (Benin et al., 2006; NCI, 2019), we propose that message sharing can also be considered an important behavioral consequence of perceived source trust. Especially when faced with newly emerged diseases such as COVID-19 and insufficient knowledge, people have to rely on perceived source trust (Kumkale et al., 2010) when deciding whether to share COVID-19 messages. As a result, trusting a source should be positively associated with intentions to share information from that source. Therefore, we pose the following hypothesis:

Hypothesis 1: The higher the perceived source trust, the more likely people will share COVID-19 information from that source.

The Underlying Psychological Processes

Perceived source trust might be associated with sharing intentions through different underlying psychological processes. From a cognitive perspective, people will evaluate the behavior (information sharing in this case) before they perform it and they are more likely to engage in a behavior if it will lead to positive outcomes as compared to negative ones (Fishbein & Ajzen, 2011; Rosenstock, 1974). For example, beliefs about favorable versus unfavorable outcomes of a behavior will affect behavioral intentions according to the theory of reasoned action (Fishbein & Ajzen, 2011). Similarly, beliefs about the benefits versus barriers of performing a behavior can predict behavioral intentions in the health belief model (Rosenstock, 1974).

We expect that the theoretical insights above can also be applied when the target behavior is health information sharing: Positive and negative beliefs about sharing will mediate the relationship between source trust and source-specific information sharing intentions. It is important to note that although the approach is labeled as “reasoned,” the beliefs about the behavior might not be accurate or bias-free (Geraerts et al., 2008). Instead, beliefs related to information sharing are largely anchored by people’s predispositions and preexisting attitudes. For example, research found that when political news stories reflected liberal values, Democrats were more likely than Republicans to perceive the news story as important and believable, and thus were more likely to share those messages (Su et al., 2019).

In the context of the COVID-19 pandemic, however, given that people lacked strong preexisting attitudes or knowledge about the disease, perceived source trust may play a more prominent role in anchoring people’s formation of positive and negative beliefs about sharing COVID-19 information, which then shapes information sharing intentions. Therefore, perceiving health professionals as a trusted source of COVID-19 information will increase positive beliefs (e.g., sharing COVID-19 information will help people prevent the disease) and decrease negative beliefs (e.g., sharing COVID-19 information is a waste of time and efforts) about sharing. Subsequently, positive beliefs will increase sharing intentions while negative beliefs may undermine such intentions. Accordingly, we propose the mediation hypothesis below:

Hypothesis 2: Beliefs about sharing will mediate the effects of source trust on source-specific COVID-19 information sharing intentions such that perceived source trust will affect beliefs about sharing, which in turn will affect sharing intentions.

Apart from beliefs about sharing, another potential mediator concerns emotions. Emotions refer to the psychological states that represent valenced responses to objects and events (Nabi, 2010). As discussed, trusting a source will make readers more influenced by its messages (Kumkale et al., 2010; Pornpitakpan, 2004). Cognitive appraisal theory suggests that emotions are triggered by cognitive evaluations of the situations/events described in messages: in the context of the COVID-19 pandemic, whereas positive-valenced information about the disease may lead to such positive emotions as hopefulness, optimism, and confidence, negative-valenced information about the disease may result in such negative emotions as anxiety, anger, fear, and sadness (Lazarus, 1991; Roseman, 1984).

Research found that emotions predicted and explained behaviors beyond beliefs and attitudes (Allen et al., 1992; Perugini & Bagozzi, 2001). Emotions can influence what people learn and recall, as well as judgment and decision-making (Forgas, 2006). Subsequently, people may choose to share COVID-19-related information and thereby connect to other people on the topic due to the positive and negative emotions they feel (Rimé, 2007, 2009). In the process, studies on negativity bias suggest that negative emotions may play a more prominent role in motivating behaviors (message sharing in this case) than positive emotions (Baumeister et al., 2001; Rozin & Royzman, 2001). It is because people tend to give more weight to the negative aspects of an issue/event than its positive counterparts in decision-making (Kahneman & Tversky, 1984; Peeters & Czapinski, 1990) and can more easily identify and recall negative emotions than positive ones (Ben-Zéev & Revhon, 2004; Wilhelm et al., 2004). Because negative emotions can increase people’s attention, involvement, and levels of psychological arousal about an issue (Berger, 2011; Berger & Milkman, 2012), they are more likely than positive emotions to motivate information sharing (Heath, 1996; Luminet et al., 2000).

Accordingly, we argue that the perceived trust of a COVID-19 information source will affect positive emotions (optimism, hopefulness, and confidence) and negative emotions (anger, annoyance, anxiety, fear, and sadness) about the disease felt by people, which in turn will be associated with sharing intentions. Hence, a mediation hypothesis is proposed below:

Hypothesis 3: Emotions will mediate the effects of source trust on source-specific COVID-19 information sharing intentions such that perceived source trust will affect emotions, which in turn will affect sharing intentions.

Method

Participants

We constructed and managed the online questionnaire for data collection using SoJump (http://www.sojump.com), the largest online survey platform in China. Chinese adult respondents were recruited to answer our survey questionnaire on SoJump to receive monetary compensation during March and early April of 2020. Until mid-March 2020, the majority of the confirmed cases of COVID-19 were in China (125,048 cases globally with 80,981 cases in China on March 13, 2020), which attracted a high volume of public health attention and coverage (WHO, 2020a). Therefore, given when the study was conducted, using a Chinese sample can better serve the study’s purpose due to the prominence of the disease in China. A total of 617 respondents completed the survey. After excluding respondents who did not follow the survey instructions or with missing demographics data (n = 8), the final sample size was 609. The demographics of the final sample were as follows: 54% were female and 46% were male. Almost 54% (53.9%) were aged between 18 and 30, 35.6% between 31 and 50, and 10.5% were above 50. In terms of education, 20.6% of respondents did not receive a college education, 14.8% attended a 3-year college, 44.3% attended a 4-year college or received a bachelor’s degree, and 20.4% received postgraduate education. The sample included 51.2% who were married and 80.3% who had health insurance.

Measures

Perceived COVID-19 Information Source Trust

Source trust was measured by asking the respondents the extent to which they trusted the COVID-19 information from a variety of sources, each on a 7-point scale from 1 = not trusted at all to 7 = extremely trusted. These COVID-19 information sources included government agencies (M = 5.67, SD = 1.37), health professionals (M = 5.80, SD = 1.30), academic institutions (M = 5.68, SD = 1.24), news media (M = 4.84, SD = 1.51), social media (M = 3.95, SD = 1.53), family (M = 4.65, SD = 1.40), and friends (M = 4.59, SD = 1.27).

COVID-19 Source-Specific Sharing Intentions

Intentions to share information were measured by asking the respondents their likelihood of sharing COVID-19 information from the same list of sources: government agencies (M = 5.13, SD = 1.69), health professionals (M = 5.11, SD = 1.61), academic institutions (M = 5.02, SD = 1.65), news media (M = 4.43, SD = 1.73), social media (M = 3.61, SD = 1.78), family (M = 4.10, SD = 1.70), and friends (M = 4.08, SD = 1.64), each on a 7-point scale where 1 = not likely at all and 7 = extremely likely.

Behavioral Beliefs About Sharing

Positive beliefs about the outcomes of sharing were gauged by asking the respondents to what extent they agree or disagree with the three statements below, each on a 5-point scale from 1 = strongly disagree to 5 = strongly agree: COVID-19 information sharing will (1) raise disease awareness, (2) increase adherence to disease prevention guidelines, and (3) promote adoption of preventive measures. Responses to the aforementioned three items were averaged to create the positive behavioral beliefs index (α = .97, M = 3.91, SD = 0.88). Negative beliefs about the consequences of sharing were measured by asking the respondents to indicate their level of agreement with the following three statements, each on a 5-point scale where 1 = strongly disagree and 5 = strongly agree: COVID-19 information sharing will (1) spread misinformation, (2) make people panic, and (3) be a waste of time and efforts. A negative behavioral beliefs index about sharing was derived by averaging the three responses (α = .80, M = 3.15, SD = 0.93).

Emotions

Respondents were asked the extent to which they felt each of the following emotions when deciding whether to share COVID-19 information, each on a 5-point scale from 1 = not at all to 5 = extremely. A negative emotion index was created by averaging responses to the five negative emotion items including anger, annoyance, anxiety, fear, and sadness (α = .87, M = 2.95, SD = 0.85). A positive emotion index was derived by averaging responses to the three positive emotion items including optimism, hopefulness, and confidence (α = .94, M = 3.60, SD = 0.89).

Control Variables

Respondents’ risk perceptions of COVID-19, frequency of exposure to messages about COVID-19, and their demographics (including age, gender, education, income, marital status, and insurance status) were controlled in the data analysis.

Results

Overall, a repeated-measures ANOVA with Greenhouse-Geisser correction revealed that COVID-19 information sources received different levels of perceived trust, F(3, 2069) = 276.30, p < .001. Specifically, health professionals (M = 5.80, SD = 1.20) were the most-trusted source, followed by academic institutions (M = 5.68, SD = 1.24), government agencies (M = 5.67, SD = 1.37), news media (M = 4.84, SD = 1.51), family (M = 4.65, SD = 1.40), and friends (M = 4.59, SD = 1.27). Social media (M = 3.95, SD = 1.53) were rated as the least trusted source of the COVID-19 information (Table 1). Results for pairwise comparisons are specified in Table 1.

Table 1.

Relationships Between Source Trust and Source-Specific Sharing Intentions (N = 609).

COVID-19 information source	Source trust	Source-specific sharing intentions	Relationships between source trust and sharing intentions
Health professionals	5.80^a (1.20)	5.11^a (1.61)	.64*** (.55, .74)
Academic institutions	5.68^b (1.24)	5.02^b (1.65)	.63*** (.53, .72)
Government agencies	5.67^b (1.37)	5.13^a,b (1.69)	.70*** (.62, .78)
News media	4.84^c (1.51)	4.43^c (1.73)	.77*** (.70, .85)
Family	4.65^c,d (1.40)	4.10^d (1.70)	.78*** (.71, .86)
Friends	4.59^d (1.27)	4.08^d (1.64)	.82*** (.73, .90)
Social media	3.95^e (1.53)	3.61^e (1.78)	.82*** (.75, .88)

Note. Source trust: Mean (SD); Sharing intentions: Mean (SD). Values in the source trust column with different superscripts differ from each other at p < .05 level. Values in the source-specific sharing intentions column with different superscripts differ from each other at p < .05 level. Relationships between source trust and source-specific sharing intentions were tested controlling for demographics, risk perceptions, and the COVID-19 message exposure. Beta coefficients and 95% confidence intervals were reported. ***p < .001.

Similarly, a repeated-measures ANOVA with Greenhouse-Geisser correction found that messages from different COVID-19 information sources also differed in their likelihood of being shared, F(3, 1766) = 193.76, p < .001. Specifically, health professionals (M = 5.11, SD = 1.61) and government agencies (M = 5.13, SD = 1.69) were the sources from which the COVID-19 information was the most likely to be shared, followed by academic institutions (M = 5.02, SD = 1.65), news media (M = 4.43, SD = 1.73), family (M = 4.10, SD = 1.70), and friends (M = 4.08, SD = 1.64). Sharing COVID-19 information from social media (M = 3.61, SD = 1.78) was rated as the least likely (Table 1). Results for pairwise comparisons are specified in Table 1.

To address Hypothesis 1 about the relationship between source trust and source-specific sharing intentions, multivariate regression analysis found that COVID-19 information source trust was positively associated with COVID-19 information sharing intentions after controlling for demographics, risk perceptions, and the COVID-19 message exposure, as shown in Table 1 (B = 0.64, p < .001, for health professionals; B = 0.63, p < .001, for academic institutions; B = 0.70, p < .001, for government agencies; B = 0.77, p < .001, for news media; B = 0.78, p < .001, for family; B = 0.82, p < .001, for friends; and B = 0.82, p < .001, for social media). Therefore, Hypothesis 1 received support.

To address Hypotheses 2 and 3 regarding the mediating roles of behavioral beliefs about sharing and emotions, mediation analyses were conducted with Hayes’ macro (Hayes, 2013). The mediation model is depicted in Figure 1. Results revealed differences in the underlying psychological mechanisms for sharing COVID-19 information across different sources (Table 2).

Figure 1.

The mediation model.

Table 2.

Mediation Effects: Source Trust on Source-Specific Sharing Intentions Through Different Mediators (Beta, 95% Bootstrap CI).

Mediators	Health professionals	Academic institutions	Government agencies	News media	Family	Friends	Social media
Positive beliefs	0.048 (0.010, 0.096)	0.042 (0.004, 0.085)	0.049 (0.020, 0.085)	0.015 (−0.011, 0.044)	0.012 (−0.014, 0.039)	0.007 (−0.020, 0.034)	0.003 (−0.013, 0.021)
Negative beliefs	0.001 (−0.006, 0.008)	0.002 (−0.005, 0.011)	0.001 (−0.005, 0.008)	0.000 (−0.004, 0.006)	0.007 (−0.003, 0.021)	0.003 (−0.004, 0.015)	0.010 (−0.000, 0.024)
Positive emotions	0.015 (−0.012, 0.047)	0.010 (−0.009, 0.035)	0.010 (−0.010, 0.032)	−0.001 (−0.015, 0.012)	0.000 (−0.013, 0.012)	0.001 (−0.012, 0.014)	−0.004 (−0.015, 0.003)
Negative emotions	0.009 (−0.016, 0.036)	0.001 (−0.022, 0.024)	0.001 (−0.016, 0.020)	0.023 (0.007, 0.045)	0.035 (0.013, 0.061)	0.050 (0.024, 0.084)	0.038 (0.017, 0.065)

Note. The mediation paths (indirect effects) were tested using the bootstrap approach with 10,000 bootstrap samples controlling for demographics, risk perceptions, and the COVID-19 message exposure. Beta coefficients and 95% bootstrap confidence intervals were reported. Significant mediation paths were bolded.

Among the highly trusted sources (i.e., health professionals, academic institutions, and government agencies), source trust increased positive beliefs about sharing, which in turn increased sharing intentions. These indirect effects were tested using a bootstrap approach with 10,000 bootstrap samples: B = 0.048, bootstrap confidence interval (CI) [0.010, 0.096] for health professionals; B = 0.042, bootstrap CI [0.004, 0.085] for academic institutions; and B = 0.049, bootstrap CI [0.020, 0.085] for government agencies. Specifically, perceived trust of health professionals (B = 0.23, p < .001), academic institutions (B = 0.21, p < .001), and government agencies (B = 0.17, p < .001) as the source of the COVID-19 information increased positive beliefs about sharing, which in turn were positively associated with intentions to share information from these sources (B = 0.21, p = .005, for health professionals; B = 0.20, p = .011, for academic institutions; and B = 0.29, p < .001, for government agencies). Thus, findings supported Hypothesis 2. Indirect effects regarding other information sources with beliefs about sharing as the mediator were statistically nonsignificant (Table 2).

By comparison, among sources with medium or low levels of trust (i.e., news media, family, friends, and social media), perceived source trust increased negative emotions, which in turn increased information sharing intentions. These indirect effects were tested using a bootstrap approach with 10,000 bootstrap samples: B = 0.023, bootstrap CI [0.007, 0.045] for news media; B = 0.035, bootstrap CI [0.013, 0.061] for family members; B = 0.050, bootstrap CI [0.024, 0.084] for friends; and B = 0.038, bootstrap CI [0.017, 0.065] for social media. Specifically, trusting news media (B = 0.08, p < .001), family members (B = 0.11, p < .001), friends (B = 0.14, p < .001), and social media (B = 0.14, p < .001) as the source of the COVID-19 information increased negative emotions, which in turn were positively associated with intent to share information from these sources (B = 0.28, p < .001, for news media; B = 0.33, p < .001, for family members; B = 0.36, p < .001, for friends; and B = 0.28, p < .001, for social media). Therefore, Hypothesis 3 also received support. Indirect effects regarding other information sources with positive and negative emotions as the mediators were statistically nonsignificant (Table 2).

Discussion

This study investigated the relationship between perceived COVID-19 information source trust and source-specific information sharing intentions while also addressing behavioral beliefs about sharing and emotions as mediators. Findings from the study have implications on understanding source related health information sharing behaviors as well as the underlying mental processes involved.

First, findings suggest that health professionals, government agencies, and academic institutions were rated as more trusted COVID-19 information sources than news media, family, and friends whereas social media were rated as the least trusted source. These results echoed previous studies examining the perceived trust of general health information sources (not about a specific disease) where health professionals and government agencies were found to be the most trusted sources (Dutta-Bergman, 2003; NCI, 2019). Thus, it shows that people tend to trust the same sources for both general health information and information about specific diseases. More important, the sharing intentions of source-specific COVID-19 information revealed a similar pattern with perceived source trust: information from health professionals, government agencies, and academic institutions were more likely to be shared as compared to information from news media, family members, and friends. The COVID-19 information from social media had the lowest sharing likelihood. As a result, it is not surprising that we found strong positive associations between perceived COVID-19 information source trust and source-specific information sharing intentions. Past studies demonstrated that perceived source trust was a positive predictor of issue attitudes and behaviors, and that people were more likely to be influenced by messages from trusted sources especially when they lacked sufficient knowledge about the issue (Benin et al., 2006; Kumkale et al., 2010; Pornpitakpan, 2004). Our current study extended this line of reasoning and showed that perceived source trust was also positively associated with health information sharing in the context of the COVID-19 pandemic.

Moreover, we investigated the underlying psychological mechanisms of COVID-19 information sharing and found that beliefs about sharing and emotions mediated the relationship between perceived source trust and source-specific information sharing intentions. Specifically, among highly trusted sources including health professionals, government agencies, and academic institutions, source trust increased source-specific sharing intentions through making people believe that sharing will lead to positive outcomes (e.g., COVID-19 information sharing will increase people’s disease awareness and promote prevention behaviors). This finding was consistent with the reasoned action approach to human behavior which emphasized that people tend to evaluate the outcomes of behavior when deciding whether to perform it (Fishbein & Ajzen, 2011; Geraerts et al., 2008). Thus, given that positive beliefs mediated the sharing of COVID-19 messages from health professionals, academic institutions, and government agencies, these health information sources should encourage readers to share their COVID-19 messages and make the sharing option easy so that their messages will become more available and accessible for the general public through information sharing.

By comparison, this study also found that among COVID-19 information sources with medium or low levels of perceived trust, which include news media, family, friends, and social media, perceived source trust increased source-specific sharing intentions through triggering negative emotions such as anxiety, anger, and fear. As trusting a source makes readers more affected by its messages, our findings suggest that COVID-19 information from news media, family, friends, and social media is more likely to trigger negative emotions in readers, which coincided with the fact that negative emotions tend to spread in social circles and group settings: among family members, coworkers/friends, and on social media (Cheshin et al., 2011; Hatfield et al., 1994; Kramer et al., 2014). Negative emotion contagion is detrimental to psychological well-being and it also makes people feel less prepared or in control facing the COVID-19 pandemic (Barsade, 2020). However, the awareness that people share information from family members, friends, and social media out of emotional reasons over positive beliefs can help prevent negative emotion contagion or the diffusion of misinformation. As negative emotion contagion is often an unconscious process like racial stereotyping, awareness of this process can reduce its harmful influence (Burns et al., 2017). Recognizing that negative emotion contagion is likely during the current public health crisis, people need to be more careful when sharing negative-valenced information and avoid sharing unverified information. Meanwhile, readers need to be more cautious with highly emotional messages that circulate in their social circles and on social media.

This study has several limitations. First, the sample is skewed toward younger adults in China. Because the severity of the COVID-19 tends to increase with age, rendering the disease riskier for the elderly population (Centers for Disease Control and Prevention, 2020), future studies should strive for more demographically representative samples or oversample population subgroups to see if our findings can be replicated. Second, we only measured respondents’ self-reported information sharing intentions instead of recording actual sharing behaviors. Thus, it remains a question if people who indicate strong information-sharing intentions will actually share COVID-19 messages in the real world. Finally, this study is cross-sectional and the causal directions between the variables are deduced per theories, not study design. Future research can use longitudinal designs to better establish the causal directions regarding the mediation paths (psychological mechanisms underlying health information sharing) proposed by the study.

Notwithstanding the limitations mentioned above, this study contributes to our understanding of health communication about the COVID-19 pandemic by demonstrating that perceived source trust predicted source-specific sharing. More important, positive beliefs about sharing motivated sharing of COVID-19 information from highly trusted sources whereas negative emotions triggered the sharing of information from sources with medium or low levels of perceived trust. These study results will inform media/health professionals and the public of the different mental processes behind source-specific health information sharing behaviors and have practical significance on improving the quality of the health messages being shared and preventing negative emotion contagion during a public health crisis.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Linqi Lu

Jiawei Liu

References

Allen

C. T.

Machleit

K. A.

Kleine

S. S.

(1992). A comparison of attitudes and emotions as predictors of behavior at diverse levels of behavioral experience. Journal of Consumer Research, 18(4), 493–504. https://doi.org/10.1086/209276

Barsade

(2020, March 26). The contagion we can control. Harvard Business Review. https://hbr.org/2020/03/the-contagion-we-can-control

Baumeister

R. F.

Bratslavsky

Finkenauer

Vohs

K. D.

(2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370. https://doi.org/10.1037/1089-2680.5.4.323

Benin

A. L.

Wisler-Scher

D. J.

Colson

Shapiro

E. D.

Holmboe

E. S.

(2006). Qualitative analysis of mothers’ decision-making about vaccines for infants: The importance of trust. Pediatrics, 117(5), 1532–1541. https://doi.org/10.1542/peds.2005-1728

Ben-Zéev

Revhon

(2004). Emotional complexity in everyday life. Social Science Information, 43(4), 581–589. https://doi.org/10.1177/0539018404047704

Berger

(2011). Arousal increases social transmission of information. Psychological Science, 22(7), 891–893. https://doi.org/10.1177/0956797611413294

Berger

Milkman

(2012). What makes online content viral? Journal of Marketing Research, 49(2), 192–205. https://doi.org/10.1509/jmr.10.0353

Burns

M. D.

Monteith

M. J.

Parker

L. R.

(2017). Training away bias: The differential effects of counterstereotype training and self-regulation on stereotype activation and application. Journal of Experimental Social Psychology, 73, 97–110. https://doi.org/10.1016/j.jesp.2017.06.003

Centers for Disease Control and Prevention. (2020). Coronavirus disease 2019 (COVID-19). https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/older-adults.html

10.

Cheshin

Rafaeli

Bos

(2011). Anger and happiness in virtual teams: Emotional influences of text and behavior on others’ affect in the absence of non-verbal cues. Organizational Behavior and Human Decision Processes, 116(1), 2–16. https://doi.org/10.1016/j.obhdp.2011.06.002

11.

Dutta-Bergman

(2003). Trusted online sources of health information: Differences in demographics, health beliefs, and health-information orientation. Journal Medical Internet Research, 5(3), e21. https://doi.org/10.2196/jmir.5.3.e21

12.

Fishbein

Ajzen

(2011). Predicting and changing behavior: The reasoned action approach. Psychology Press.

13.

Forgas

J. P.

(2006). Affective influences on interpersonal behavior: Towards understanding the role of affect in everyday interactions. In Forgas

J. P.

(Ed.), Affect in social thinking and behavior (pp. 269–290). Psychology Press.

14.

Geraerts

Bernstein

D. M.

Merckelbach

Linders

Raymaeckers

Loftus

E. F.

(2008). Lasting false beliefs and their behavioral consequences. Psychological Science, 19(8), 749–753. https://doi.org/10.1111/j.1467-9280.2008.02151.x

15.

Hatfield

Cacioppo

Rapson

R. L.

(1994). Emotional contagion. Cambridge University Press.

16.

Hayes

A. F.

(2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. Guilford.

17.

Heath

(1996). Do people prefer to pass along good news or bad news? Valence and relevance of news as a predictor of transmission propensity. Organizational Behavior and Human Decision Processes, 68(2), 79–94. https://doi.org/10.1006/obhd.1996.0091

18.

Johns Hopkins University Coronavirus Resource Center. (n.d.). COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). https://coronavirus.jhu.edu/map.html

19.

Kahneman

Tversky

(1984). Choices, values, and frames. American Psychologist, 39(4), 341–350. https://doi.org/10.1037/0003-066X.39.4.341

20.

Kelly

Hornik

Romantan

Schwartz

J. S.

Armstrong

DeMichele

Fishbein

Gray

Hull

Kim

Nagler

Niederdeppe

Susana Ramirez

Smith-McLallen

Wong

(2010). Cancer information scanning and seeking in the general population. Journal of Health Communication, 15(7), 734–753. https://doi.org/10.1080/10810730.2010.514029

21.

Kramer

A. D.

Guillory

J. E.

Hancock

J. T.

(2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences of the United States of America, 111(24), 8788–8790. https://doi.org/10.1073/pnas.1320040111

22.

Kumkale

G. T.

Albarracín

Seignourel

P. J.

(2010). The effects of source credibility in the presence or absence of prior attitudes: Implications for the design of persuasive communication campaigns. Journal of Applied Social Psychology, 40(6), 1325–1356. https://doi.org/10.1111/j.1559-1816.2010.00620.x

23.

Lazarus

R. S.

(1991). Emotion and adaptation. Oxford University Press.

24.

Luminet

Bouts

Delie

Manstead

A. S. R.

Rimé

(2000). Social sharing of emotion following exposure to a negatively valenced situation. Cognition and Emotion, 14(5), 661–688. https://doi.org/10.1080/02699930050117666

25.

Nabi

R. L.

(2010). The case for emphasizing discrete emotions in communication research. Communication Monographs, 77(2), 153–159. https://doi.org/10.1080/03637751003790444

26.

National Cancer Institute. (2019). HINTS Brief 39: Trust in health information sources among American adults. https://hints.cancer.gov/docs/Briefs/HINTS_Brief_39.pdf

27.

Nature . (2020). Coronavirus: The first three months as it happened. https://www-nature-com-s.web.bisu.edu.cn/articles/d41586-020-00154-w

28.

Niederdeppe

Hornik

R. C.

Kelly

B. J.

Frosch

D. L.

Romantan

Stevens

R. S.

Barg

F. K.

Weiner

J. L.

Sanford Schwartz

(2007). Examining the dimensions of cancer-related information seeking and scanning behavior. Health Communication, 22(2), 153–167. https://doi.org/10.1080/10410230701454189

29.

O’Keefe

D. J.

(2015). Persuasion: Theory and research. Sage Publications.

30.

Peeters

Czapinski

(1990). Positive-negative asymmetry in evaluations: The distinction between affective and informational negativity effects. European Review of Social Psychology, 1(1), 33–60. https://doi.org/10.1080/14792779108401856

31.

Perugini

Bagozzi

R. P.

(2001). The role of desires and anticipated emotions in goal-directed behaviours: Broadening and deepening the theory of planned behaviour. British Journal of Social Psychology, 40(1), 79–98. https://doi.org/10.1348/014466601164704

32.

Pornpitakpan

(2004). The persuasiveness of source credibility: A critical review of five decades’ evidence. Journal of Applied Social Psychology, 34(2), 243–281. https://doi.org/10.1111/j.1559-1816.2004.tb02547.x

33.

Rimé

(2007). The social sharing of emotion as an interface between individual and collective processes in the construction of emotional climates. Journal of Social Issues, 63(2), 307–322. https://doi.org/10.1111/j.1540-4560.2007.00510.x

34.

Rimé

(2009). Emotion elicits the social sharing of emotion: Theory and empirical review. Emotion Review, 1(1), 60–85. https://doi.org/10.1177/1754073908097189

35.

Roseman

I. J.

(1984). Cognitive determinants of emotion: A structural theory. Review of Personality & Social Psychology, 5, 11–36.

36.

Rosenstock

I. M.

(1974). Historical origins of the health belief model. Health Education Monographs, 2(4), 328–335. https://doi.org/10.1177/109019817400200403

37.

Rozin

Royzman

E. B.

(2001). Negativity bias, negativity dominance, and contagion. Personality and Social Psychology Review, 5(4), 296–320. https://doi.org/10.1207/S15327957PSPR0504_2

38.

M. H.

Liu

McLeod

D. M.

(2019). Pathways to news sharing: Issue frame perceptions and the likelihood of sharing. Computers in Human Behavior, 91, 201–210. https://doi.org/10.1016/j.chb.2018.09.026

39.

Wigfall

L. T.

Friedman

D. B.

(2016). Cancer information seeking and cancer-related health outcomes: A scoping review of the health information national trends survey literature. Journal of Health Communication, 21(9), 989–1005. https://doi.org/10.1080/10810730.2016.1184358

40.

Wilhelm

Schoebi

Perrez

(2004). Frequency estimates of emotions in everyday life from a diary method’s perspective: A comment on Scherer et al.’s survey-study “emotions in everyday life.” Social Science Information, 43(4), 647–665. https://doi.org/10.1177/0539018404047712

41.

World Health Organization. (2020a). Coronavirus disease 2019 (COVID-19): Situation report—18. https://www.who.int/docs/default-source/searo/timor-leste/13-03-2020-tls-sitrep-18-ncov-eng.pdf?sfvrsn=9df102cd_2

42.

World Health Organization. (2020b). Coronavirus in China. https://www.who.int/china/emergencies/coronavirus-(covid-19)

43.

World Health Organization. (2020c). Naming the coronavirus disease (COVID-19) and the virus that causes it. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/naming-the-coronavirus-disease-(covid-2019)-and-the-virus-that-causes-it

44.

Zhao

Mao

Kreps

G. L.

Chou

S. W. Y.

Perkosie

Nie

Song

Kim

(2015). Cancer information seekers in China: A preliminary profile. Journal of Health Communication, 20(5), 616–626. https://doi.org/10.1080/10810730.2015.1012244