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Article

Empirical Study on Social Media Exposure and Fear as Drivers of Anxiety and Depression during the COVID-19 Pandemic

by
Xiao Gu
1,
Bojan Obrenovic
2,3 and
Wei Fu
4,*
1
Media Literacy Research Institute, Communication University of Zhejiang, Hangzhou 310018, China
2
Zagreb School of Economics and Management, 10000 Zagreb, Croatia
3
Luxembourg School of Business, 2453 Luxembourg, Luxembourg
4
Fine Arts College, Shanghai Normal University, Shanghai 201418, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(6), 5312; https://doi.org/10.3390/su15065312
Submission received: 26 January 2023 / Revised: 10 March 2023 / Accepted: 14 March 2023 / Published: 16 March 2023
(This article belongs to the Section Health, Well-Being and Sustainability)
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Abstract

:
The COVID-19 pandemic has resulted in an abundance of news and information dominating media outlets, leading to a widespread atmosphere of fear and uncertainty, potentially having adverse effects on mental health. This study aims to explore whether social media exposure contributes to anxiety and depression. An online cross-sectional survey was conducted using a standardized questionnaire to collect data on social media exposure, fear of COVID-19, depression, and anxiety from 327 employed individuals in the United States. Structural equation modeling was employed to analyze the relationships between social media exposure, fear of COVID-19, anxiety, and depression. The results suggest that fear of COVID-19 leads to anxiety and depression, and that social media exposure leads to fear, anxiety, and depression. These findings highlight the potential adverse effects of social media exposure and fear on mental health and suggest that reducing social media exposure could help minimize anxiety levels. It also emphasizes the significance of understanding the impact of fear of COVID-19 on anxiety and depression and provides guidance for managing and coping with fear in this pandemic. This study’s relevance lies in gaining critical insights into the pros and cons of using social media for health-related information during a pandemic. The novelty of this study lies in its unique perspective on the impact of adverse information that has distinct psychological and social implications.

1. Introduction

The COVID-19 pandemic has been a global hazard to public psychological well-being, as it has wreaked havoc on economies, destroyed jobs, jeopardized health, and put entire livelihoods in jeopardy [1]. Even with the advent of technological [2,3] and medical advancements [4,5,6,7], as well as techno-medical inventions [8,9,10], humanity has found itself facing a huge challenge with the COVID-19 virus. Despite the relatively prompt development of vaccines and ongoing research [11], the virus remained a persistent threat. News of the pandemic crisis overflowed both traditional and new media, and reports of approaching risks, hazards, vulnerability, and prevention spread rapidly on social media channels [12]. This included not only the communication of scientific advancement announcements, risk assessments, and government reports, but also misinformation, fake news, opinions, fabrications, and misstatements [13]. Such developments can be harmful to physical and mental health [14]. Any misunderstanding of the threat’s severity, susceptibility, or prolonged exposure to faulty information can lead to virus exposure or poor prevention [15]. Online misinformation is among the most well documented and widespread phenomena that peaked at the onset of the current pandemic and set in motion an immense mental health crisis [16,17]. Clinicians and health professionals, as well as the general public, refer to the COVID crisis and its effect on health as the “9/11 of healthcare systems” [18].
Although reports on the exponential increase in recorded mood disorders during similar disasters, such as COVID-19, have previously been disclosed and discussed in the literature [19,20,21], such occurrences were primarily discussed as direct repercussions of monetary or health-related concerns [22] and were not examined with regard to social media content. In this paper, we examine the link between fear of the COVID-19 pandemic and adverse psychological effects owing to fear development, with a focus on subjective components originating from the media frenzy. Anxiety represents a natural bodily warning system that is reflected in the feeling of worry and unease over an uncertain outcome [23,24]. It is an intense emotion marked by tension and recurring intrusive worrisome thoughts or concerns. To counteract the emotion, afflicted individuals often engage in avoidance. Anxiousness may occur in response to unforeseeable events, uncertain futures, or specific events [25]. Depressive disorder is defined by a prolonged poor mood, as well as self-destructive tendencies and underlying stress and anxiety, in broad psychiatric terms [26,27].
Several studies on risk communication have been undertaken to explore media influence on risk perceptions and coping [19,20,28,29,30]. However, in light of the recent global health crisis, there is now considerable concern about the effect of negative and inaccurate news co-creation and infodemics that has an unprecedented effect on psychological well-being [31,32,33]. This study fills the existing research gaps by exploring how exposure to such information through new channels determines perceived threat, anxiety, and fear. The research objective of this paper is the examination of social media as an advantageous tool for crisis communication and mental health practitioners. We provide additional insight into cognitive processing and affective reactions, followed by mental impairment, in the context of a pandemic.
Our findings shed light on how the public perceives and responds to information about the pandemic acquired through social media. This knowledge can be used to better communicate public health messages and increase the public understanding of COVID-19. Further implications are that our evidence highlights the potential impact of social media on public health during hardship and traumatic events and stresses the importance of developing strategies to promote the responsible use of social platforms during pandemics and other public health emergencies. The novelty of this study lies in its unique perspective on the impact of adverse information that has distinct psychological and social implications. A special contribution to the growing body of knowledge has been made by focusing narrowly on the upheaval and new media that allowed for a nuanced understanding of the complex interplay between social media exposure and mental health outcomes. This study takes a more specialized approach by examining behavioral and psychological impacts and mental health outcomes in the context of emergency, rather than just in general, thus providing important insights into the unique mental health challenges that individuals face during uncertainty. Additionally, the study uncovers specific mechanisms underlying the negative reactions leading to impairment during peril by uncovering how exposure affects individuals’ perceptions of risk, their coping strategies, and their access to social support. This gives way to a whole new avenue of future research agendas. A majority of existing studies on infodemics and false information related to social media and the pandemic have presupposed the frequency of usage and exposure as an antecedent to fear [33,34,35]. The originality of this study stems from the notion that social media, when understood correctly, need not be the driver of cognitive impairment but rather serves as a platform for open dialogue, wherein adverse effects of exposure to misinformation can be countered by discussion and plurality of opinion. Additional value can be found in rendering a critical evaluation of two well-established theoretical models and empirical evidence that are often invoked when studying media effects on psychological health. Consistently with research on the advancement and enhancement of AI-supported technologies for informing, forecasting, and predicting the pandemic’s impact [36], the relevance of this study can be translated into gaining critical apprehension of the amenities and drawbacks of using social media for health-related informing on the pandemic.
The structure of this paper is as follows. First, we discuss and critically evaluate the most prominent theories and models for the analysis of media impact and psychological coping during health emergencies. Additionally, we provide a short overview of the key concepts used to develop the research model. Next, we explain the materials and methods used, followed by the results and discussion. Finally, we present our concluding remarks and the implications of the empirical analysis. We present the limitations, implications, and recommendations for future research lastly.

2. Theoretical Background and Research Model Development

The application of social media in the health organizational literature is increasing in an accelerated manner due to its capacity to transcend geographical barriers that would otherwise impede the admission of healthcare resources [37]. AI-supported technologies with advanced algorithms not only serve as a means to spread the news and obtain information from social media that would later be used for diagnosing and forecasting pandemics [36], but also aid in predicting mental disorders [37] and detecting behavioral signs of anxiety with over 90% accuracy [38]. Health and medicine operators are required to optimize online consumer experiences. Growing reports in the news suggest a growing population suffering from isolation [39], experiencing both concerns over future outcomes [40], such as loss of monetary income [41], and a lack of autonomy [42,43]. Psychological distress arising from the outbreak combined with mediated misrepresentation of reality led to the deterioration of psychological well-being.
Nonetheless, people have also chosen to either underestimate the severity of the threat and inadvertently risk infection, or shunned all news on crisis developments, both of which have had negative effects on public welfare [44]. As a result, the psychological effect is twofold: individuals either engage in discussions and consume content, much of which may be entirely inaccurate but nevertheless elicits debilitating emotions, such as extreme and exaggerated fear which can lead to maladaptive coping [45]; or, they are constantly targeted by stories, anecdotes, news, discussions, and facts to the point where the mere presence of the information elicits aversion [46,47]. During a similar crisis, few researchers have addressed these effects thoroughly in the existing literature; however, the majority of publications has been limited to prevention and resilience [48], as well as drivers for policy adherence, educational campaign responsiveness, and the desire to engage in preventive activity. A few key drivers are shaping the public’s perception of the infection’s true threat.
The current research critically evaluates two prominent and widely used research approaches, namely, cultivation theory and the extended parallel processing model (EPPM) [49]. We applied the two as the theoretical ground for framing our research hypothesis. While we grant their fruitfulness and soundness, we are aware of limitations and shortfalls befalling both frameworks [50,51]. More specifically, the cultivation theory is deemed suitable due to the acknowledgment of the social and cultural context in shaping media effects, but it fails to account for individual differences in susceptibility and response, and it lacks the ground to explain the dynamic and interactive nature of social media [52,53]. We find dialogue and content co-creation to be one of the key drivers of emotional responses fueled by both information and misinformation. Considering there is no limitation of user-generated content and/or call to the authority, interactivity is more likely to generate false news and infodemics and thus elicit a higher fear response. The EPPM model is well supported and effective for understanding how perceived threats and efficacy in fear appeal influence behavior and cognition [54]. Considering it is often used in health and protective campaigns, it is also crucial for comprehending the prevalence of fear and anxiety in the context of COVID-19. However, similarly to the cultivation theory, it falls short of explaining the complexity of individual responses to fear appeals. While the model offers a plausible explanation for why users engage in counterproductive behaviors, it cannot fully capture the nuanced ways in which social media exposure impacts mental health [55]. For instance, the model deems maladaptive coping, such as avoidance, reasonable in the face of extreme fear. However, users often engage in frequent and prolonged consumption of fear-inducing content, regardless of its adverse effects. They may become overly engaged in discussions, which render all actions ineffective, thus reducing the perceived efficacy.
A person’s conduct and psychological condition are influenced not just by emotions such as fear, but also by perceptions [56]. The latter is determined by a person’s beliefs about the intended goal as a result of their altered conduct and their belief in their capacity to modify their action [57]. The influence of media campaigns on preventative behavior has been studied using the extended parallel processing model [48,58]. The model relates to how the conjunction of rational considerations and emotions determines behavioral decisions. The success of messages conveyed to enforce protective behavior depends on the level of perceived threat regarding a health issue [59]. Some authors have incorporated media-induced anxiety. However, these studies were conducted to improve crisis communication [60,61,62] and to find efficient ways of delivering crucial information on COVID-19 that would drive public behavior in the appropriate protective direction [63,64,65]. Intermediary variables for theoretical and clinical conceptualizations in psychology and psychiatry were often overlooked in attempts to design instrumental plans. The current crisis provides a rare outlook on sociopsychological drivers of anxiety and depression resulting from artificially cultivated social media infodemics [66].
The rapid dissemination of outbreak-related content was enabled by new technologies, making all parties both stakeholders and bearers of news, whether it was reliable scientific accounts or circumstantial fabrications. Examining the role of social media in molding public risk perceptions and determining the extent to which media depictions can raise panic to the point of dysfunctionality, resulting in anxiety and depression, is essential. We will go into greater detail on how social media exposure contributes to dysfunctional coping in the face of adversity by inducing fear and anxiety [67].

2.1. Social Media Exposure and Mental Health

A dialogue-oriented, open, interactional aspect of social media may be useful in a non-critical, everyday setting, as it serves to raise or lessen the credibility of propaganda accounts from traditional media [68]. However, the spread of unverified information will negatively impact public psychological welfare during naturally indeterminate periods heightened by anguish, disquiet, and distrust. Biased and discriminatory media can develop a skewed sense of social reality, which can sabotage public health professionals’ efforts to stop the virus from spreading and bring the public’s misery under control [69]. Information overload causes the “infodemics effect” [70], which greatly raises dread and anxiety even when there are no reliable news [46,47]. When there is a lack of reliable information, a person’s mind may seek out any information, even if it comes from insufficient resources, in order to gain a fictitious sensation of control that will appear to alleviate uncertainty-related stress. According to the basic tenets of the cultivation theory, mediated virtual reality shapes public perceptions of impending dangers and susceptibility to objectively existing threats that may be amplified or downplayed [71], and this has been demonstrated on multiple occasions during previous health crises, such as the H1N1 [72], bovine [73], and avian flus [74]. Activity, time spent, addiction, and investment have been found to be connected with anxiety and psychological distress [75]. Time spent on social media was strongly connected with the risk perception of H1N1 infection [76], and this finding was reproduced during COVID-19 [44]. According to Bae and Yoo (2015) [77], during the MERS epidemic, the most often mentioned words in the media were specific preventive behavior-oriented concepts, such as wearing masks, hand sanitizer, and avoiding crowded locations. Furthermore, using social media was associated with dispositional anxiety [78] and anxiety among adolescents [79,80]. Therefore, we suggest the following hypothesis:
Hypothesis 1.
Social media exposure during the COVID-19 pandemic leads to increased anxiety.

2.2. Social Media Exposure and Fear of COVID-19

Prolonged exposure to media coverage has such an impact on the human psyche that terror exacerbated by media portrayals occurs before the actual hardship [81]. As Nair et al. (2020) [82] and Yu et al. (2021) [83] have already pointed out, relying on media information amid adversity is nothing new. It is not surprising that the public obtains COVID-19 pandemic-related material via social media [84], despite the fact that it paradoxically makes people more fearful [44]. The media can not only impact risk perceptions but can also be used to anticipate fear across nations [85]. For hazard emergency communication, raising awareness, and disseminating preventative measures, social media has become indispensable [73]. It is vital to stress the importance of media intake in the generation of anxiety. The fear of contracting H1N1 influences the amount of time spent on social media [76]. Garfin et al. [44] reached identical outcomes when it came to COVID-19. The media’s role in shaping public risk perception has long been recognized [86,87]. According to Van Den Bulck and Custers (2009) [81], fear of the virus-like H5N1 may develop far earlier than the infection itself. Preventive behavior is either encouraged or discouraged by fear [88]. Scutto et al. (2021) [54] argue that since false information regularly circulates across social media, extensive false information is more likely to influence false memories [89,90], and such memories are affected by depressive and anxious symptoms. Therefore, we suggest the following hypothesis:
Hypothesis 2.
Social media exposure during the COVID-19 pandemic leads to increased fear of COVID-19.

2.3. Fear of COVID-19 and Anxiety

Anxiety strengthens the effect of the stimulus-driven attentional system over the goal-driven system, thus counterproductively increasing the threat susceptibility. It diverts from fear in that it is a future-oriented and long-lasting response aimed at diffusing threats, while fear is considered a present-oriented short-term response to a specific threat. The pandemic has aroused an increase in anxiousness and psychological distress among populations globally [91,92,93]. General uncertainty, fear, and disquiet are currently being recorded, measured, and tested to fathom the large-scale psychological deterioration and long-term impacts on mental health [94,95,96]. In prior epidemics, the virus led to an increase in psychological distress and mental impairment [97,98,99]. High rates of fear of COVID-19 leading to anxiety and related disorders have been recorded worldwide [100,101,102].
Given the negative impact of misleading information on psychological well-being as well as direct objective losses resulting from global health concerns, a unified approach across domains is critical for risk communication [103]. The lack of solid information or being bombarded with competing reports on how to best take safety precautions and cope with disease, isolation, stigmatization, and financial hardships leads to uncertainty and anxiety [104].
Fear, as a result of repeated coverage in the media and on social media, significantly precedes the actual pandemic circumstances, according to a previous study on infectious disease reporting [85,105,106]. Media exposure can be seen as a forerunner to a lot of distress and a cause of nervous diseases [81]. While a small amount of fear might trigger the preservation response and inspire protective action, prolonged stress and worry can have negative consequences [107,108]. Such a situation cannot be exploited as an incentive since people tend to shut down when they are under too much stress, rendering all behaviors useless [109,110]. The link between pandemic dread and anxiety has been proven in previous analytical and empirical studies [111,112,113]. Fighting uncertainty’s terror depletes more energy than the ambiguity itself.
Hypothesis 3.
Fear of COVID-19 leads to an increase in anxiety.

2.4. Fear of COVID-19 and Depression

Depressive disorder is defined by a prolonged poor mood, as well as self-destructive tendencies and underlying stress and anxiety, in broad psychiatric terms [26,27]. Affected individuals report an inability to perform properly in one or more critical compartments, including personal, social, and occupational domains, as well as excessive concern and compulsive fixation with current distress [114]. Impositions from society pose a threat to one’s identity and individuality [115]. Hopelessness and a threat to the self are indicators of emotions and coping, according to Cypryaska and Nezlek’s empirical study (2020) [116], implying that the risk to identity can affect the behavioral outcome under adversity. Furthermore, a volatile environment and continuous dread drain individuals’ sense of control, support, and social validation [117], robbing them of self-confidence and jeopardizing their very social identity as defined by attachment to the community [115]. The existing literature on earlier disasters [118,119] and negative mental health consequences such as depressive episodes, tiredness, and insomnia substantiate similar assertions [120,121].
COVID-19 has had a tumultuous and uncertain course. Infection exacerbated the atmosphere of uncertainty and existential dread [117,122,123], resulting in economic insecurity and panic over the loss of critical resources such as possible accomplishments, relatedness, and monetary resource attainments for meeting essential physiological needs [124,125]. Restrictions and impositions, on the other hand, are stressful and debilitating, as they breed marginalization, isolation, and social rejection [126]. All efforts are rendered ineffective for an uprising situation when a high threat is seen without the proper response efficacy [110,127], and they result in pessimism. Dissociation, pathological disengagement, weariness, trauma, loss of interest in formerly relevant stakes, and general indifference are all symptoms of a lack of social control, support, and safety [128].
Hypothesis 4.
Fear of COVID-19 leads to an increase in depression.

3. Materials and Methods

3.1. Participants and Procedure

This cross-sectional survey was conducted using a standardized questionnaire to collect data from academic staff working in institutions across different faculties in the USA. A survey is a suitable research design to gather information on people’s thoughts, beliefs, and attitudes. Purposive sampling was employed to select the participants for this study based on their professional experience and expertise in their respective fields. Before data collection, informed consent was obtained from the study participants, who were informed that the survey was completely anonymous. The data collected included information on demographics, social media exposure, and fear of COVID-19, depression, and anxiety. After eliminating incomplete responses, the analysis was carried out on a sample of 327 respondents.

3.2. Measurements

3.2.1. Fear of COVID-19

The Fear of COVID-19 scale was adopted from Blakey et al. (2015) [129], who used it to assess fear of Ebola. The scale was adapted from Wheaton et al. (2012) [130], which was used during the H1N1 (swine flu) epidemic. The Fear of COVID-19 scale consists of nine items that were assessed on a five-point Likert scale ranging from “Not at all” (1) to “Very much” (5). Cronbach’s alpha was of an acceptable level (α = 0.75). Sample questions included: “How likely are you to become infected with COVID-19?”; “How likely is it that someone you know may contract COVID-19?”; and “How quickly do you think COVID-19 contamination will spread in your country?”.

3.2.2. Social Media Exposure

The social media exposure scale adopted from Ng et al. (2018) was used to assess social media exposure during a pandemic (α = 0.92) [131]. Sample items included: “I saw many pictures regarding COVID-19 being shared on my social media such as Facebook, Twitter, Instagram, etc.”; “I saw many posts that relate to health information about COVID-19 that were shared by people in my social network”; and “I saw many people making comments on others’ status updates about COVID-19”. The items were evaluated on a five-point Likert scale ranging from “Strongly disagree” (1) to “Strongly agree” (5).

3.2.3. Depression

The frequency of depression symptoms was measured using The Center for Epidemiology Scale for Depression (CES-D) (Radloff, 1977), created by the National Institute of Mental Health for epidemiological research [132]. A scale consisting of 20 items was assessed from “rarely or none of the time (<1 week)” (1) to “almost or all of the time (the whole month)” (5). Cronbach’s alpha was of an acceptable level (α = 0.85). Sample items included: “I thought my life had been a failure”; “I felt fearful”; and “My sleep was restless”.

3.2.4. Anxiety

The seven-item Generalized Anxiety Disorder (GAD-7) scale was applied to assess general anxiety and worry symptoms. It has shown adequate reliability in past studies [48,133] and in this study (α = 0.91). The GAD-7 scale consists of seven items, each evaluated from “not at all” (1) to “nearly every day” (5). Respondents were asked to rate how often they were affected by specific issues in the previous month. The items included “not being able to stop or manage worry” and “being easily angry or impatient.”

4. Results

Statistical Analysis

This study examines the impact of social media exposure on anxiety and fear of COVID, as well as the effects of fear of COVID on anxiety and depression (see Figure 1). The statistical analysis was conducted using SPSS AMOS version 23. The SEM analysis was appropriate as it allowed for the exploration of links between multiple variables simultaneously. SEM was also suitable for testing the hypothesized relationships between variables, providing a more robust analysis of the data. Missing values were excluded and subsequent statistical analysis was performed on the remaining data. The final sample size consisted of 327 respondents. The sample size is adequate, considering the number of tested relationships within the model [134]. A total of 51.8% of the respondents were male and 48.2% were female. In terms of age structure, 70.7% were over 50 years old, 25.5% were between the ages of 30 and 50, and the rest were between 25 and 30. A total of 90% of the respondents held a postgraduate degree, while 10% held a bachelor’s degree.
Descriptive statistics were calculated first and the results can be found in Table 1. A non-parametric Kolmogorov–Smirnov test was performed to confirm the normality of the data. Moreover, a reliability test was executed. Table 2 shows the results of composite reliability (CR), average variance extracted (AVE), and correlation matrix determination, which confirmed discriminant and convergent validity. The AVE values were above 0.5, and the CR was above 0.9 with a threshold of 0.7, indicating convergent validity. The AVE was found to be larger than the maximum shared variance, indicating discriminant validity (MSV). A check for multicollinearity was also performed, indicating no multicollinearity concerns.
The hypothesized model was then tested using confirmatory factor analysis (CFA) with maximum likelihood estimation. Figure 2 depicts a four-factor model consisting of Depression, Anxiety, Fear of COVID, and Social Media Exposure. Several goodness-of-fit indices were calculated, including χ2/df (normed chi-square statistic), RMSEA, CFI, and SRMR, and are displayed in Table 3. At the initial step, both RMSEA and SRMR were above the proposed thresholds, indicating the need for improvements to the model. Following Fornell and Larcker (1981a, b) [135,136], components with low factor loadings, such as Depression 12–17, were removed to improve the model fit. In addition, covariances between Depression error components 3 and 5, and 6 and 19, were added based on high values of modification indices (MI) if items could be interpreted in a synonymous way [5].
As the next step, path analysis was conducted to examine the relationships between Depression, Anxiety, Fear of COVID, and Social Media Exposure in the structural model testing with SEM (Figure 3). The goodness-of-fit indices revealed that the model needed to be improved. A high modification index (MI) between e1 and e2 was found, and a covariance between these two factors was included. The addition of the link between Social Media Exposure and Anxiety greatly improved the model fit, as seen in Figure 4. A path between Social Media Exposure and Depression was also introduced, but the model fit was inadequate (CMIN/DF = 7.3). Consequently, the path between those variables was not included.
The model fit was evaluated next. The goodness-of-fit indices revealed that the model needed to be improved. Between e1 and e2, the study of covariances revealed a high modification index (MI). It was decided to include the covariance between these two factors. The addition of the link between Social Media Exposure and Anxiety also improved the model fit greatly, as seen in Figure 4. The path between Social Media Exposure and Depression was also introduced; however, the model fit was inadequate (CMIN/DF = 7.3). Consequently, the path between those variables was not included.
The-goodness-of-fit was then evaluated again, and the SRMR, CFI, and Pclose all indicated excellent model fit (Table 4). RMSEA was slightly above the 0.06 criterion, but still within acceptable bounds (χ2/df = 5.3; CFI = 0.99; SRMR = 0.06; RMSEA = 0.066). Figure 4 illustrates the modified model.
After assessing the model fit, regression analysis was performed. Standardized parameter estimates, standard errors, and p values for the structural model were calculated and displayed in Table 5. Three different levels of significance were used to determine the statistical significance of the parameter estimates (p < 0.1, p < 0.05, and p < 0.01). The analysis revealed that Fear of COVID (β = 0.222; p < 0.001) and Social Media Exposure (β = 0.094; p < 0.05) have positive and significant effects on Anxiety. Consequently, Hypotheses 1 and 3 are accepted. Furthermore, Social Media Exposure had a significant impact on Fear of COVID (β = 0.222; p < 0.1). Accordingly, Hypothesis 2 is accepted. The analysis also showed that as predicted, Fear of COVID positively impacts Depression (β = 0.17; p < 0.05), so Hypothesis 4 is accepted.

5. Discussion

The advent of social media ushered in a new era in which interactive coverage became widely available to all, with features such as commenting, sharing, reposting, blogging, and polls [83]. The issue of false news and new media has grown in prominence. People grow more worried and fearful of an impending threat, which becomes more tangible and inexorable due to the imminence of exposure. Our findings suggest that social media exposure has a positive effect on anxiety. This is in line with the findings of Su et al. (2021) [46] and Kouzy et al. (2020) [47], who discovered that information distortion causes fear and anxiety. The findings are consistent with those of Cho et al. (2020) [69], whose investigation raised the worry that erroneous perceptions of social reality impair fear control. On social media, all viewpoints have an equal opportunity of reaching a large audience, even if they are unjustified, frequently false, contrived, misleading, falsified, or exaggerated. According to Bomlitz and Brezis (2008) [87] and Garfin et al. (2020) [44], the potential of media to evoke emotional responses and influence risk perceptions is the justification for excessive anxiety of exposure. People spend significantly more time on social media sites than they do reading legitimate news sources. In line with Mesch et al. (2013) [76], we expected that time spent consuming media may predict fear, and that extended exposure would lead to increasing fright.
Furthermore, we confirmed the positive effect of social media exposure on fear of COVID-19. Therefore, Hypothesis 2, stating that social media exposure during COVID-19 induces the fear of COVID, is accepted. This is in line with the research of Garfin et al. (2020) [44] and conforms to the theory that media portrayals can exacerbate feelings of terror before actual hardship is experienced [81], especially as individuals rely heavily on media information amidst difficult times.
Many previous disasters, whether natural, financial, or health-related, have demonstrated that psychological distress causes impairment. Safety evaluation, threat susceptibility and severity perception, infobesity, fear of social isolation, exclusion, and/or quarantine are all stressors for the general population. We hypothesized that such major distress would have ramifications for one’s anxiety levels to the point that the affected population would meet the diagnostic criteria for an anxiety mood disorder, based on the existing psychological and psychiatric body of knowledge and the most recent advancements. As a result, Hypothesis 3 is accepted, stating that fear of COVID-19 has a positive effect on anxiety, as predicted by Ornell et al. (2020) [111] and Hamouche (2020) [112]. Our findings support Chiu et al. (2020) [113], Perlis (2020) [107], and Xiang et al. (2020) [108].
Hypothesis 4, which claims that the fear of COVID-19 has a positive effect on depression, is accepted. Traumatic occurrences, such as the COVID-19 pandemic, cause extreme dread and, as a result, an unpleasant, terrible, and agonizing sense of hopelessness. These findings align with Jahangiry et al. (2020) [117], Killgore and Cloonan (2020) [122], and Tull et al. (2020) [123], supporting Cypryanska and Nezlek’s (2020) [116] findings, as well as Obrenovic et al.’s (2021) [137] and Aguilar-Quintana’s (2021) [138].
This study aimed to examine the intensity of the maladaptive fear response elicited by social media during adversity. Our findings contribute to an advanced understanding of how social media exposure and fear could have an adverse effect on mental health. The findings are of great use to practitioners and psychologists. Understanding that fear of COVID-19 significantly contributes to anxiety and depression enables individuals to apply techniques for managing and coping with existing fear. Social media exposure should be minimized, as it can contribute to increased anxiety, especially during a crisis when there is an overflow of information on one topic. The study’s relevance lies in gaining a critical apprehension of the amenities and drawbacks of using social media for health-related information during the pandemic. To our knowledge, this is one of the few studies that investigates the relationships between social media exposure, fear, anxiety, and depression in the context of the COVID pandemic, and its contribution to the literature on the topic is significant.

6. Conclusions

The COVID-19 pandemic caused an unprecedented wave of panic and anxiety. This study explored how anxiety and depression have manifested themselves in the face of repeated and extensive exposure to social media content and information overload, which we applied in previously uncharted territory—the pandemic context and with reference to social media. Our research model, consisting of the relationships between social media exposure, fear of COVID, anxiety, and depression, was tested on a sample of 327 employees in the USA. Fear of COVID leads to anxiety and depression, whereas social media exposure has an adverse effect on fear and anxiety. The effects of the pandemic are still present and the consequences on mental health are evident in everyday life. Anxious and depressed individuals are neither model citizens nor productive workers. Thus, common efforts by the government, scientists, and practitioners are warranted to alleviate the effects of the pandemic on mental health.
The current study offers a fresh perspective on the impact of social media on public health during challenging times, such as the COVID-19 pandemic. This study highlights the need for interdisciplinary collaboration between the government, scientists, and practitioners to develop effective interventions to alleviate the effects of the pandemic on mental health. With mental health issues likely to persist long after the pandemic ends, common efforts are necessary to support individuals’ mental health and well-being. By narrowing its focus to the psychological and social implications of adverse information, this study sheds light on the complex interplay between social media exposure and mental health outcomes. This study emphasizes the need for social media platforms to take responsibility for managing the spread of misinformation during public health emergencies. Social media companies can use these findings to develop responsible policies and guidelines to help users navigate pandemic-related information effectively. It is essential for individuals to be mindful of the amount and quality of information they consume through social media and other sources. The implication of our research is that it is crucial for people to take breaks from social media and seek information from reliable sources to reduce the negative effects of social media exposure on mental health. One of the major contributions of the current study is the establishment of the impact of social media exposure on increased fear of COVID-19. This finding sheds light on how the public perceives and responds to pandemic-related information acquired through social media. This knowledge can be used to better communicate public health messages and increase the public understanding of COVID-19. This study’s implication is that it highlights the potential impact of social media on public health during hardship and traumatic events, stressing the importance of developing strategies to promote the responsible use of social media platforms during pandemics and other public health emergencies.
Our study utilized a robust methodology which included a sizable sample and statistical techniques to establish the extents of relationships. Furthermore, the study’s findings may make a significant contribution to the field by revealing particular mechanisms that underlie the adverse reactions that lead to impairment during times of crisis. Specifically, this study explored how social media exposure can influence individuals’ perception of risk, coping strategies, and access to social support. The identification of these mechanisms provides a novel direction for future research that can further enhance our understanding of the relationship between social media, mental health, and crisis situations.
Moreover, there are some significant policy implications for health officials and policymakers. Firstly, prior knowledge may mediate the relationship between social media exposure and fear of COVID. Users with more extensive prior knowledge of the topic are less likely to be affected by false news. Therefore, protective campaigns should aim to educate the public using facts. If cognitive styles are related to the association, information disclosed by health officials should aim to create messages that stimulate analytical thinking and critical appraisal. Since political beliefs are also likely to affect the relationship between exposure and mental health, social media should be used to identify groups with specific political orientations, and these groups should be targeted with custom-tailored campaigns that appeal to their belief systems.

7. Limitations and Future Studies

This study is subject to limitations as it relies solely on self-reported data. To validate the findings, future studies should incorporate triangulation. Furthermore, the study was cross-sectional, and a longitudinal study would help evaluate how depression and anxiety develop over time under the influence of long-term social media exposure. The sample consisted only of the adult population. However, during the pandemic, teenagers and children were heavily affected as they used social media as a replacement for physical socialization with their peers.
Moreover, the impact of social media on depression needs further investigation, either through fear of COVID as a mediator or through other factors such as social isolation. If social norms were a decisive component in preventing the danger, it is reasonable to expect that the prospect of a communicable sickness would elicit a similar emotional and coping reaction. Additionally, since we focused on the nurturing effect, we believe that future research should build on our work and conduct comparable assessments of message development and dissemination during emergencies on certain social media platforms.

Author Contributions

Conceptualization, X.G. and B.O.; data curation, X.G.; formal analysis, X.G. and W.F.; investigation, X.G. and B.O.; methodology, B.O. and W.F.; project administration, X.G. and B.O.; resources, X.G. and B.O.; writing—original draft, X.G. and B.O.; writing—review and editing, X.G. and W.F. All authors have read and agreed to the published version of the manuscript.

Funding

National Social Science Foundation of China “The Study of Poetry in the Eastern Zhou Dynasty from the Perspective of Interaction between Classical Interpretation and Philosophical Construction” (grant No. 20BZX057).

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethical Review Board of Zagreb’s School of Economics and Management under the approval code 1001.

Informed Consent Statement

Informed consent was obtained from all the subjects involved in the study.

Data Availability Statement

The data presented in this study are available upon request from the corresponding author due to restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Demirtaş-Madran, H.A. Accepting Restrictions and Compliance with Recommended Preventive Behaviors for COVID-19: A Discussion Based on the Key Approaches and Current Research on Fear Appeals. Front. Psychol. 2021, 12, 558437. [Google Scholar] [CrossRef]
  2. Li, T.; Li, Y.; Hoque, M.A.; Xia, T.; Tarkoma, S.; Hui, P. To What Extent We Repeat Ourselves? Discovering Daily Activity Patterns Across Mobile App Usage. IEEE Trans. Mob. Comput. 2022, 21, 1492–1507. [Google Scholar] [CrossRef]
  3. Qin, X.; Ban, Y.; Wu, P.; Yang, B.; Liu, S.; Yin, L.; Liu, M.; Zheng, W. Improved Image Fusion Method Based on Sparse Decomposition. Electronics 2022, 11, 2321. [Google Scholar] [CrossRef]
  4. Liu, H.; Liu, M.; Li, D.; Zheng, W.; Yin, L.; Wang, R. Recent Advances in Pulse-Coupled Neural Networks with Applications in Image Processing. Electronics 2022, 11, 3264. [Google Scholar] [CrossRef]
  5. Yang, B.; Li, Y.; Zheng, W.; Yin, Z.; Liu, M.; Yin, L.; Liu, C. Motion prediction for beating heart surgery with GRU. Biomed. Signal Process. Control. 2023, 83, 104641. [Google Scholar] [CrossRef]
  6. Li, H.; Peng, R.; Wang, Z. On a Diffusive Susceptible-Infected-Susceptible Epidemic Model with Mass Action Mechanism and Birth-Death Effect: Analysis, Simulations, and Comparison with Other Mechanisms. SIAM J. Appl. Math. 2018, 78, 2129–2153. [Google Scholar] [CrossRef]
  7. Xiong, Z.; Weng, X.; Wei, Y. SandplayAR: Evaluation of psychometric game for people with generalized anxiety disorder. Arts Psychother. 2022, 80, 101934. [Google Scholar] [CrossRef]
  8. Pan, Z.-Y.; Zhong, H.-J.; Huang, D.-N.; Wu, L.-H.; He, X.-X. Beneficial Effects of Repeated Washed Microbiota Transplantation in Children with Autism. Front. Pediatr. 2022, 10, 971. [Google Scholar] [CrossRef]
  9. Lu, S.; Yang, B.; Xiao, Y.; Liu, S.; Liu, M.; Yin, L.; Zheng, W. Iterative reconstruction of low-dose CT based on differential sparse. Biomed. Signal Process. Control. 2023, 79, 104204. [Google Scholar] [CrossRef]
  10. Ban, Y.; Wang, Y.; Liu, S.; Yang, B.; Liu, M.; Yin, L.; Zheng, W. 2D/3D Multimode Medical Image Alignment Based on Spatial Histograms. Appl. Sci. 2022, 12, 8261. [Google Scholar] [CrossRef]
  11. Hu, F.; Qiu, L.; Xia, W.; Liu, C.-F.; Xi, X.; Zhao, S.; Yu, J.; Wei, S.; Hu, X.; Su, N.; et al. Spatiotemporal evolution of online attention to vaccines since 2011: An empirical study in China. Front. Public Health 2022, 10, 2310. [Google Scholar] [CrossRef]
  12. Tsoy, D.; Tirasawasdichai, T.; Kurpayanidi, K.I. Role of Social Media in Shaping Public Risk Perception during COVID-19 Pandemic: A Theoretical Review. Int. J. Manag. Sci. Bus. Adm. 2021, 7, 35–41. [Google Scholar] [CrossRef]
  13. Van Bavel, J.J.; Baicker, K.; Boggio, P.S.; Capraro, V.; Cichocka, A.; Cikara, M.; Willer, R. Using social and be-havioral science to support the COVID-19 pandemic response. Nat. Hum. Behav. 2020, 4, 460–471. [Google Scholar] [CrossRef] [PubMed]
  14. Torales, J.; O’Higgins, M.; Castaldelli-Maia, J.M.; Ventriglio, A. The outbreak of COVID-19 coronavirus and its impact on global mental health. Int. J. Soc. Psychiatry 2020, 66, 317–320. [Google Scholar] [CrossRef] [Green Version]
  15. Giorgi, G.; Lecca, L.I.; Alessio, F.; Finstad, G.L.; Bondanini, G.; Lulli, L.G.; Arcangeli, G.; Mucci, N. COVID-19-Related Mental Health Effects in the Workplace: A Narrative Review. Int. J. Environ. Res. Public Health 2020, 17, 7857. [Google Scholar] [CrossRef]
  16. Brennen, J.S.; Simon, F.M.; Howard, P.N.; Nielsen, R.K. Types, Sources, and Claims of COVID-19 Misinformation. Reuters Institute. 2020. Available online: https://reutersinstitute.politics.ox.ac.uk/types-sources-and-claims-covid-19-misinformation (accessed on 2 February 2021).
  17. O’Connor, C.; Murphy, M. Going viral: Doctors must tackle fake news in the covid-19 pandemic. BMJ 2020, 24, m1587. [Google Scholar] [CrossRef]
  18. Carmassi, C.; Cerveri, G.; Bertelloni, C.A.; Marasco, M.; Dell’Oste, V.; Massimetti, E.; Gesi, C.; Dell’Osso, L. Mental health of frontline help-seeking healthcare workers during the COVID-19 outbreak in the first affected hospital in Lombardy, Italy. Psychiatry Res. 2021, 298, 113763. [Google Scholar] [CrossRef]
  19. He, X.; Zhang, Y.; Chen, M.; Zhang, J.; Zou, W.; Luo, Y. Media Exposure to COVID-19 Predicted Acute Stress: A Moderated Mediation Model of Intolerance of Uncertainty and Perceived Social Support. Front. Psychiatry 2020, 11, 613368. [Google Scholar] [CrossRef] [PubMed]
  20. Huang, Q. How does news media exposure amplify publics’ perceived health risks about air pollution in china? a conditional media effect approach. Int. J. Commun. 2020, 14, 20. [Google Scholar]
  21. Li, Q.; Miao, Y.; Zeng, X.; Tarimo, C.S.; Wu, C.; Wu, J. Prevalence and factors for anxiety during the coronavirus disease 2019 (COVID-19) epidemic among the teachers in China. J. Affect. Disord. 2020, 277, 153–158. [Google Scholar] [CrossRef]
  22. Hu, F.; Qiu, L.; Xi, X.; Zhou, H.; Hu, T.; Su, N.; Zhou, H.; Li, X.; Yang, S.; Duan, Z.; et al. Has COVID-19 Changed China’s Digital Trade?—Implications for Health Economics. Front. Public Health. 2022, 10, 831549. [Google Scholar] [CrossRef] [PubMed]
  23. Hooley, J.M.; Butcher, J.N.; Matthew, K.N.; Mineka, S. Abnormal Psychology; Pearson: Boston, MA, USA, 2016. [Google Scholar]
  24. Barnett, P.A.; Mackintosh, B.; Sapountzis, A. Paying attention to anxiety: Attentional control and anxiety in children. J. Anxiety Disord. 2009, 23, 222–230. [Google Scholar]
  25. Peteet, J.R. COVID-19 anxiety. J. Relig. Health 2020, 59, 2203–2204. [Google Scholar] [CrossRef]
  26. Sveen, J. Anticipatory anxiousness: Conceptual and empirical findings. Cogn. Emot. 2007, 21, 715–734. [Google Scholar]
  27. Sveen, J. Cognitive appraisal of anxiety in anticipation of an event. Anxiety Stress Coping 2008, 21, 163–178. [Google Scholar]
  28. Lee, Y.; Jeon, Y.J.; Kang, S.; Shin, J.I.; Jung, Y.-C.; Jung, S.J. Social media use and mental health during the COVID-19 pandemic in young adults: A meta-analysis of 14 cross-sectional studies. BMC Public Health 2022, 22, 995. [Google Scholar] [CrossRef] [PubMed]
  29. Marciano, L.; Ostroumova, M.; Schulz, P.J.; Camerini, A.-L. Digital Media Use and Adolescents’ Mental Health During the Covid-19 Pandemic: A Systematic Review and Meta-Analysis. Front. Public Health 2022, 9, 2208. [Google Scholar] [CrossRef]
  30. Wang, Q.; Xie, L.; Song, B.; Di, J.; Wang, L.; Mo, P.K.-H. Effects of Social Media Use for Health Information on COVID-19–Related Risk Perceptions and Mental Health During Pregnancy: Web-Based Survey. JMIR Public Health Surveill. 2022, 10, e28183. [Google Scholar] [CrossRef]
  31. Nascimento, I.J.B.D.; Pizarro, A.B.; Almeida, J.; Azzopardi-Muscat, N.; Gonçalves, M.A.; Björklund, M.; Novillo-Ortiz, D. Infodemics and health misinformation: A systematic review of reviews. Bull. World Health Organ. 2022, 100, 544–561. [Google Scholar] [CrossRef]
  32. Balakrishnan, V.; Zhen, N.W.; Chong, S.M.; Han, G.J.; Lee, T.J. Infodemic and fake news–A comprehensive overview of its global magnitude during the COVID-19 pandemic in 2021: A scoping review. Int. J. Disaster Risk Reduct. 2022, 78, 103144. [Google Scholar] [CrossRef]
  33. Malik, A.; Bashir, F.; Mahmood, K. Antecedents and Consequences of Misinformation Sharing Behavior among Adults on Social Media during COVID-19. SAGE Open 2023, 13, 21582440221147022. [Google Scholar] [CrossRef] [PubMed]
  34. Mohammed, F.; Al-Kumaim, N.H.; Alzahrani, A.I.; Fazea, Y. The Impact of Social Media Shared Health Content on Protective Behavior against COVID-19. Int. J. Environ. Res. Public Health 2023, 20, 1775. [Google Scholar] [CrossRef] [PubMed]
  35. Pang, H.; Ji, M.; Hu, X. How Differential Dimensions of Social Media Overload Influences Young People’s Fatigue and Negative Coping during Prolonged COVID-19 Pandemic? Insights from a Technostress Perspective. Healthcare 2023, 11, 6. [Google Scholar] [CrossRef] [PubMed]
  36. Comito, C.; Pizzuti, C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused re-view. Artif. Intell. Med. 2022, 128, 102286. [Google Scholar] [CrossRef]
  37. Comito, C.; Falcone, D.; Talia, D. A Peak Detection Method to Uncover Events from Social Media. In Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, 19–21 October 2017; pp. 459–467. [Google Scholar] [CrossRef]
  38. Yan, W.J.; Ruan, Q.N.; Jiang, K. Challenges for Artificial Intelligence in Recognizing Mental Disorders. Diagnostics 2022, 13, 2. [Google Scholar] [CrossRef]
  39. Ducharme, J. COVID-19 is Making America’s Loneliness Epidemic Even Worse. 2020. Available online: https://time.com/5833681/loneliness-covid-19/ (accessed on 20 May 2020).
  40. Blow, C.M. COVID-19, Confusion, and Uncertainty. 2020. Available online: https://www.nytimes.com/2020/03/15/opinion/coronavirus-fear.html (accessed on 25 May 2020).
  41. Frazier, L. Survey Tracks How COVID-19 Is Affecting American’s Finances, with Grim Results. 2020. Available online: https://www.forbes.com/sites/lizfrazierpeck/2020/04/14/survey-tracks-how-covid-19-is-affecting-americans-finances-with-grim-results/ (accessed on 10 August 2020).
  42. Willson, J. The impact of income loss: Stories from the front lines. J. Poverty 2020, 24, 494–513. [Google Scholar]
  43. Beer, T. Anti-Mask Rallies Continue in US Amid Rising Coronavirus Cases and Deaths. 2020. Available online: https://www.forbes.com/sites/tommybeer/2020/07/16/anti-mask-rallies-continue-in-us-amid-rising-coronavirus-cases-and-deaths/?sh=6278c2922246 (accessed on 10 August 2020).
  44. Garfin, D.R.; Silver, R.C.; Holman, E.A. The novel coronavirus (COVID-2019) outbreak: Amplification of public health consequences by media exposure. Health Psychol. 2020, 39, 355–357. [Google Scholar] [CrossRef]
  45. Karmakar, A.; Dutta, S.; Chakraborty, A.; Mukherjee, A.; Bhattacharya, J.; Malas, B.; Chaudhuri, A. Construction of a scale on coping repertoire during COVID-19 pandemic. J. Behav. Sci. 2021, 16, 114–130. [Google Scholar]
  46. Su, S.; Du, L.; Jiang, S. Learning from the past: Development of safe and effective COVID-19 vaccines. Nat. Rev. Genet. 2021, 19, 211–219. [Google Scholar] [CrossRef]
  47. Kouzy, R.; Jaoude, J.A.; Kraitem, A.; El Alam, M.B.; Karam, B.; Adib, E.; Zarka, J.; Traboulsi, C.; Akl, E.; Baddour, K. Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter. Cureus 2020, 12, e7255. [Google Scholar] [CrossRef] [Green Version]
  48. Tsoy, D.; Godinic, D.; Tong, Q.; Obrenovic, B.; Khudaykulov, A.; Kurpayanidi, K. Impact of Social Media, Extended Parallel Process Model (EPPM) on the Intention to Stay at Home during the COVID-19 Pandemic. Sustainability 2022, 14, 7192. [Google Scholar] [CrossRef]
  49. Egger, C.M.; Magni-Berton, R.; Roché, S.; Aarts, K. I Do it My Way: Understanding Policy Variation in Pandemic Response Across Europe. Front. Politi- Sci. 2021, 3, 622069. [Google Scholar] [CrossRef]
  50. Sparks, G.G. Media Effects Research: A Basic Overview; Wadsworth Publications: Belmont, CA, USA, 2013. [Google Scholar]
  51. De Hoog, N.; Stroebe, W.; De Wit, J.B.F. The Impact of Vulnerability to and Severity of a Health Risk on Processing and Acceptance of Fear-Arousing Communications: A Meta-Analysis. Rev. Gen. Psychol. 2007, 11, 258–285. [Google Scholar] [CrossRef]
  52. Signorielli, N.; Morgan, M.; Shanahan, J. Cultivation Analysis. In An Integrated Approach to Communication Theory and Research, 3rd ed.; Dillard, J.P., Shen, L., Eds.; Routledge: London, UK, 2019; p. 14. [Google Scholar]
  53. Grabe, M.E.; Zhou, S.; Barnett, B. Individual differences in cultivation: How long-term television exposure influences beliefs about social reality. J. Broadcast. Electron. Media 2018, 62, 399–416. [Google Scholar]
  54. Scuotto, C.; Ilardi, C.R.; Avallone, F.; Maggi, G.; Ilardi, A.; Borrelli, G.; Gamboz, N.; La Marra, M.; Perrella, R. Objective Knowledge Mediates the Relationship between the Use of Social Media and COVID-19-Related False Memories. Brain Sci. 2021, 11, 1489. [Google Scholar] [CrossRef]
  55. Van der Pligt, J.; de Vries, N.K. Belief Importance in Expectancy-Value Models of Attitudes 1. J. Appl. Soc. Psychol. 1998, 28, 1339–1354. [Google Scholar] [CrossRef]
  56. Bandura, A. Self-efficacy: Toward a unifying theory of behavioral change. Psychol. Rev. 1977, 84, 191–215. [Google Scholar] [CrossRef]
  57. Bandura, A. Self-efficacy mechanism in human agency. Am. Psychol. 1982, 37, 122–147. [Google Scholar] [CrossRef]
  58. McMahan, S.; Witte, K.; Meyer, J. The perception of risk messages regarding electromagnetic fields: Extending the extended parallel process model to an unknown risk. Health Commun. 1998, 10, 247–259. [Google Scholar] [CrossRef]
  59. Popova, L. Extended parallel process model. In The International Encyclopedia of Media Psychology; Wiley: Hoboken, NJ, USA, 2020; pp. 1–6. [Google Scholar]
  60. Vaala, S.E.; Ritter, M.B.; Palakshappa, D.M. Experimental Effects of Tweets Encouraging Social Distancing: Effects of Source, Emotional Appeal, and Political Ideology on Emotion, Threat, and Efficacy. J. Public Health Manag. Pract. 2021, 28, E586–E594. [Google Scholar] [CrossRef]
  61. Yang, J.Z.; Chu, H. Who is afraid of the Ebola outbreak? The influence of discrete emotions on risk perception. J. Risk Res. 2018, 21, 834–853. [Google Scholar] [CrossRef]
  62. Turner, M.M.; Underhill, J.C. Motivating Emergency Preparedness Behaviors: The Differential Effects of Guilt Appeals and Actually Anticipating Guilty Feelings. Commun. Q. 2012, 60, 545–559. [Google Scholar] [CrossRef]
  63. Fauci, A.S.; Lane, H.C.; Redfield, R.R. Covid-19–Navigating the Uncharted. N. Engl. J. Med. 2020, 382, 1268–1269. [Google Scholar] [CrossRef]
  64. Khan, K.; Zhao, H.; Zhang, H.; Yang, H.; Shah, M.H.; Jahanger, A. The impact of COVID-19 pandemic on stock markets: An empirical analysis of world major stock indices. J. Asian Financ. Econ. Bus. 2020, 7, 463–474. [Google Scholar] [CrossRef]
  65. Liu, N.; Chen, Z.; Bao, G. Role of media coverage in mitigating COVID-19 transmission: Evidence from China. Technol. Forecast. Soc. Chang. 2021, 163, 120435. [Google Scholar] [CrossRef] [PubMed]
  66. García-Saisó, S.; Marti, M.; Brooks, I.; Curioso, W.H.; González, D.; Malek, V.; D’Agostino, M. The COVID-19 Infodemic. Rev. Panam. Salud Pública 2021, 45, e56. [Google Scholar] [CrossRef]
  67. Jarynowski, A.; Wójta-Kempa, M.; Płatek, D.; Czopek, K. Attempt to understand public-health relevant social dimensions of covid-19 outbreak in Poland. Soc. Regist. 2020, 4, 7–44. [Google Scholar] [CrossRef] [Green Version]
  68. Johnson, T.J.; Kaye, B.K. Wag the Blog: How Reliance on Traditional Media and the Internet Influence Credibility Perceptions of Weblogs Among Blog Users. J. Mass Commun. Q. 2004, 81, 622–642. [Google Scholar] [CrossRef]
  69. Cho, S.E.; Jung, K.; Park, H.W. Social media use during Japan’s 2011 earthquake: How Twitter transforms the locus of crisis communication. Media Int. Aust. 2013, 149, 28–40. [Google Scholar] [CrossRef]
  70. World Health Organization. Coronavirus Disease (COVID-19) Pandemic. 2020. Available online: https://www.who.int/europe/emergencies/situations/covid-19 (accessed on 20 December 2020).
  71. Ali, K.; Zain-Ul-Abdin, K.; Li, C.; Johns, L.; Ali, A.A.; Carcioppolo, N. Viruses Going Viral: Impact of Fear-Arousing Sensationalist Social Media Messages on User Engagement. Sci. Commun. 2019, 41, 314–338. [Google Scholar] [CrossRef]
  72. Oh, S.-H.; Paek, H.-J.; Hove, T. Cognitive and emotional dimensions of perceived risk characteristics, genre-specific media effects, and risk perceptions: The case of H1N1 influenza in South Korea. Asian J. Commun. 2015, 25, 14–32. [Google Scholar] [CrossRef]
  73. Manzoor, S.; Safdar, A. Cultivation of Fear Through Media: Analysis to Reveal Relationship between Perception about COVID 19 and Socio-economic Background of Media Consumers. Rev. Econ. Dev. Stud. 2020, 6, 217–228. [Google Scholar] [CrossRef]
  74. Fung, T.K.F.; Namkoong, K.; Brossard, D. Media, Social Proximity, and Risk: A Comparative Analysis of Newspaper Coverage of Avian Flu in Hong Kong and in the United States. J. Health Commun. 2011, 16, 889–907. [Google Scholar] [CrossRef] [PubMed]
  75. Keles, B.; McCrae, N.; Grealish, A. A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 2020, 25, 79–93. [Google Scholar] [CrossRef] [Green Version]
  76. Mesch, G.S.; Schwirian, K.P.; Kolobov, T. Attention to the media and worry over becoming infected: The case of the Swine Flu (H1N1) Epidemic of 2009. Sociol. Health Illn. 2013, 35, 325–331. [Google Scholar] [CrossRef] [PubMed]
  77. Han, M.; Sung, Y.-K.; Cho, S.-K.; Kim, D.; Won, S.; Choi, C.-B.; Bang, S.-Y.; Cha, H.-S.; Choe, J.-Y.; Chung, W.T.; et al. Factors Associated with the Use of Complementary and Alternative Medicine for Korean Patients with Rheumatoid Arthritis. J. Rheumatol. 2015, 42, 2075–2081. [Google Scholar] [CrossRef] [PubMed]
  78. Vannucci, A.; Flannery, K.M.; Ohannessian, C.M. Social media use and anxiety in emerging adults. J. Affect. Disord. 2017, 207, 163–166. [Google Scholar] [CrossRef] [PubMed]
  79. Tsitsika, A.K.; Tzavela, E.C.; Janikian, M.; Ólafsson, K.; Iordache, A.; Schoenmakers, T.M.; Tzavara, C.; Richardson, C. Online Social Networking in Adolescence: Patterns of Use in Six European Countries and Links with Psychosocial Functioning. J. Adolesc. Health 2014, 55, 141–147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Yan, H.; Zhang, R.; Oniffrey, T.M.; Chen, G.; Wang, Y.; Wu, Y.; Zhang, X.; Wang, Q.; Ma, L.; Li, R.; et al. Associations among Screen Time and Unhealthy Behaviors, Academic Performance, and Well-Being in Chinese Adolescents. Int. J. Environ. Res. Public Health 2017, 14, 596. [Google Scholar] [CrossRef] [Green Version]
  81. Bulck, J.V.D.; Custers, K. Television exposure is related to fear of avian flu, an Ecological Study across 23 member states of the European Union. Eur. J. Public Health 2009, 19, 370–374. [Google Scholar] [CrossRef] [Green Version]
  82. Nair, B.; Janenova, S.; Serikbayeva, B. Social and Mainstream Media Relations. In A Primer on Policy Communication in Kazakhstan; Springer: Berlin/Heidelberg, Germany, 2020; pp. 35–48. [Google Scholar] [CrossRef]
  83. Yu, M.; Li, Z.; Yu, Z.; He, J.; Zhou, J. Communication related health crisis on social media: A case of COVID-19 outbreak. Curr. Issues Tour. 2021, 24, 2699–2705. [Google Scholar] [CrossRef]
  84. Jang, K.; Baek, Y.M. When Information from Public Health Officials is Untrustworthy: The Use of Online News, Interpersonal Networks, and Social Media during the MERS Outbreak in South Korea. Health Commun. 2019, 34, 991–998. [Google Scholar] [CrossRef] [PubMed]
  85. Hansen, K.F. Approaching doomsday: How SARS was presented in the Norwegian media. J. Risk Res. 2009, 12, 345–360. [Google Scholar] [CrossRef] [Green Version]
  86. Snyder, L.B.; Rouse, R.A. The Media Can Have More Than an Impersonal Impact: The Case of AIDS Risk Perceptions and Behavior. Health Commun. 1995, 7, 125–145. [Google Scholar] [CrossRef]
  87. Bomlitz, L.J.; Brezis, M. Misrepresentation of health risks by mass media. J. Public Health 2008, 30, 202–204. [Google Scholar] [CrossRef] [PubMed]
  88. Chen, L.; Zhu, H.; Harshfield, G.A.; Treiber, F.A.; Pollock, J.S.; Pollock, D.; Okereke, O.I.; Su, S.; Dong, Y. Serum 25-Hydroxyvitamin D Concentrations Are Associated with Mental Health and Psychosocial Stress in Young Adults. Nutrients 2020, 12, 1938. [Google Scholar] [CrossRef]
  89. Lazer, D.M.J.; Baum, M.A.; Benkler, Y.; Berinsky, A.J.; Greenhill, K.M.; Menczer, F.; Metzger, M.J.; Nyhan, B.; Pennycook, G.; Rothschild, D.; et al. The science of fake news. Science 2018, 359, 1094–1096. [Google Scholar] [CrossRef]
  90. Auxier, B.; Anderson, M. Social media use in 2021. Pew Res. Cent. 2021, 1, 1–4. [Google Scholar]
  91. Li, S.; Wang, Y.; Xue, J.; Zhao, N.; Zhu, T. The Impact of COVID-19 Epidemic Declaration on Psychological Consequences: A Study on Active Weibo Users. Int. J. Environ. Res. Public Health 2020, 17, 2032. [Google Scholar] [CrossRef] [Green Version]
  92. Qiu, J.; Shen, B.; Zhao, M.; Wang, Z.; Xie, B.; Xu, Y. A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy recommendations. Gen. Psychiatry 2020, 33, e100213. [Google Scholar] [CrossRef] [Green Version]
  93. Yıldırım, M.; Geçer, E.; Akgül, Ö. The impacts of vulnerability, perceived risk, and fear on preventive behaviours against COVID-19. Psychol. Health Med. 2021, 26, 35–43. [Google Scholar] [CrossRef] [PubMed]
  94. Bao, Y.; Sun, Y.; Meng, S.; Shi, J.; Lu, L. 2019-nCoV epidemic: Address mental health care to empower society. Lancet 2020, 395, e37–e38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Tanhan, A.; Yavuz, K.F.; Young, J.S.; Nalbant, A.; Arslan, G.; Yıldırım, M.; Ulusoy, S.; Genç, E.; Uğur, E.; Çiçek, I. A Proposed Framework Based on Literature Review of Online Contextual Mental Health Services to Enhance Wellbeing and Address Psychopathology During COVID-19. Electron. J. Gen. Med. 2020, 17, em254. [Google Scholar] [CrossRef] [PubMed]
  96. Ahorsu, D.K.; Imani, V.; Lin, C.-Y.; Timpka, T.; Broström, A.; Updegraff, J.A.; Årestedt, K.; Griffiths, M.D.; Pakpour, A.H. Associations Between Fear of COVID-19, Mental Health, and Preventive Behaviours Across Pregnant Women and Husbands: An Actor-Partner Interdependence Modelling. Int. J. Ment. Health Addict. 2020, 20, 68–82. [Google Scholar] [CrossRef]
  97. de Pablo, G.S.; Vaquerizo-Serrano, J.; Catalan, A.; Arango, C.; Moreno, C.; Ferre, F.; Shin, J.I.; Sullivan, S.; Brondino, N.; Solmi, M.; et al. Impact of coronavirus syndromes on physical and mental health of health care workers: Systematic review and meta-analysis. J. Affect. Disord. 2020, 275, 48–57. [Google Scholar] [CrossRef]
  98. Maaravi, Y.; Heller, B. Not all worries were created equal: The case of COVID-19 anxiety. Public Health 2020, 185, 243–245. [Google Scholar] [CrossRef]
  99. Pappas, G.; Kiriaze, I.; Giannakis, P.; Falagas, M. Psychosocial consequences of infectious diseases. Clin. Microbiol. Infect. 2009, 15, 743–747. [Google Scholar] [CrossRef] [Green Version]
  100. Tsamakis, K.; Rizos, E.; Manolis, A.J.; Chaidou, S.; Kympouropoulos, S.; Spartalis, E.; Spandidos, D.A.; Tsiptsios, D.; Triantafyllis, A.S. [Comment] COVID-19 pandemic and its impact on mental health of healthcare professionals. Exp. Ther. Med. 2020, 19, 3451–3453. [Google Scholar] [CrossRef] [Green Version]
  101. Rossi, R.; Socci, V.; Pacitti, F.; Di Lorenzo, G.; Di Marco, A.; Siracusano, A.; Rossi, A. Mental Health Outcomes Among Frontline and Second-Line Health Care Workers During the Coronavirus Disease 2019 (COVID-19) Pandemic in Italy. JAMA Netw. Open 2020, 3, e2010185. [Google Scholar] [CrossRef]
  102. Wang, C.; Pan, R.; Wan, X.; Tan, Y.; Xu, L.; Ho, C.S.; Ho, R.C. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. Int. J. Environ. Res. Public Health 2020, 17, 1729. [Google Scholar] [CrossRef] [Green Version]
  103. Toppenberg-Pejcic, D.; Noyes, J.; Allen, T.; Alexander, N.; Vanderford, M.; Gamhewage, G. Emergency Risk Communication: Lessons Learned from a Rapid Review of Recent Gray Literature on Ebola, Zika, and Yellow Fever. Health Commun. 2019, 34, 437–455. [Google Scholar] [CrossRef]
  104. Lin, L.; McCloud, R.F.; Bigman, C.A.; Viswanath, K. Tuning in and catching on? Examining the relationship between pandemic communication and awareness and knowledge of MERS in the USA. J. Public Health 2017, 39, 282–289. [Google Scholar] [CrossRef] [Green Version]
  105. Raude, J.; Caille-Brillet, A.-L.; Setbon, M. The 2009 pandemic H1N1 influenza vaccination in France: Who accepted to receive the vaccine and why? PLoS Curr. 2010, 2, RRN1188. [Google Scholar] [CrossRef]
  106. Altheide, D.L.; Michalowski, R.S. Fear in the news: A discourse of control. Sociol. Q. 1999, 40, 475–503. [Google Scholar] [CrossRef] [Green Version]
  107. Öngür, D.; Perlis, R.; Goff, D. Psychiatry and COVID-19. JAMA 2020, 324, 1149–1150. [Google Scholar] [CrossRef] [PubMed]
  108. Liu, W.; Zhang, Q.; Chen, J.; Xiang, R.; Song, H.; Shu, S.; Chen, L.; Liang, L.; Zhou, J.; You, L.; et al. Detection of Covid-19 in Children in Early January 2020 in Wuhan, China. N. Engl. J. Med. 2020, 382, 1370–1371. [Google Scholar] [CrossRef] [PubMed]
  109. Blanuša, J.; Barzut, V.; Knežević, J. Intolerance of Uncertainty and Fear of COVID-19 Moderating Role in Relationship Between Job Insecurity and Work-Related Distress in the Republic of Serbia. Front. Psychol. 2021, 12, 647972. [Google Scholar] [CrossRef]
  110. Nelson, B.; Kaminsky, D.B. A COVID-19 crisis in US jails and prisons. Cancer Cytopathol. 2020, 128, 513. [Google Scholar] [CrossRef]
  111. Ornell, F.; Moura, H.F.; Scherer, J.N.; Pechansky, F.; Kessler, F.H.P.; von Diemen, L. The COVID-19 pandemic and its impact on substance use: Implications for prevention and treatment. Psychiatry Res. 2020, 289, 113096. [Google Scholar] [CrossRef]
  112. Hamouche, S. COVID-19 and employees’ mental health: Stressors, moderators and agenda for organizational actions. Emerald Open Res. 2020, 2, 15. [Google Scholar] [CrossRef] [Green Version]
  113. Menzies, R.E.; Menzies, R.G. Death anxiety in the time of COVID-19: Theoretical explanations and clinical impli-cations. Cogn. Behav. Ther. 2020, 13, e19. [Google Scholar] [CrossRef] [PubMed]
  114. Obrenovic, B.; Du, J.; Godinić, D.; Tsoy, D. Personality trait of conscientiousness impact on tacit knowledge sharing: The mediating effect of eagerness and subjective norm. J. Knowl. Manag. 2021, 26, 1124–1163. [Google Scholar] [CrossRef]
  115. Burke, R.M.; Midgley, C.M.; Dratch, A.; Fenstersheib, M.; Haupt, T.; Holshue, M.; Rolfes, M.A. Active Monitoring of Persons Exposed to Patients with Confirmed COVID-19–United States, January–February 2020. MMWR Morb. Mortal. Wkly. Rep. 2020, 69, 245–246. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Cypryańska, M.; Nezlek, J.B. Anxiety as a mediator of relationships between perceptions of the threat of COVID-19 and coping behaviors during the onset of the pandemic in Poland. PLoS ONE 2020, 15, e0241464. [Google Scholar] [CrossRef] [PubMed]
  117. Bakhtari, F.; Mohammadpoorasl, A.; Nadrian, H.; Alizadeh, N.; Jahangiry, L.; Ponnet, K. Determinants of hookah smoking among men in the coffee houses: An application of socio-ecological approach. Subst. Abus. Treat. Prev. Policy 2020, 15, 1–6. [Google Scholar] [CrossRef]
  118. Beatty, A.; Liao, S. Financial accounting in the banking industry: A review of the empirical literature. J. Account. Econ. 2014, 58, 339–383. [Google Scholar] [CrossRef]
  119. Kouvonen, A.; Kivimaki, M.; Oksanen, T.; Pentti, J.; De Vogli, R.; Virtanen, M.; Vahtera, J. Obesity and Occupational Injury: A Prospective Cohort Study of 69,515 Public Sector Employees. PLoS ONE 2013, 8, e77178. [Google Scholar] [CrossRef]
  120. Branas, C.C.; Kastanaki, E.A.; Michalodimitrakis, M.; Tzougas, J.; Kranioti, E.F.; Theodorakis, P.N.; Carr, B.G.; Wiebe, D.J. The impact of economic austerity and prosperity events on suicide in Greece: A 30-year interrupted time-series analysis. BMJ Open 2015, 5, e005619. [Google Scholar] [CrossRef] [Green Version]
  121. Crestani, S.; Filho, H.M.N.R.; Miguel, M.F.; De Almeida, E.X.; Santos, F.A.P. Steers performance in dwarf elephant grass pastures alone or mixed with Arachis pintoi. Trop. Anim. Health Prod. 2013, 45, 1369–1374. [Google Scholar] [CrossRef] [Green Version]
  122. Killgore, W.D.; Cloonan, S.A.; Taylor, E.C.; Dailey, N.S. Loneliness: A signature mental health concern in the era of COVID-19. Psychiatry Res. 2020, 290, 113117. [Google Scholar] [CrossRef]
  123. Tull, M.T.; Edmonds, K.A.; Scamaldo, K.M.; Richmond, J.R.; Rose, J.P.; Gratz, K.L. Psychological Outcomes Associated with Stay-at-Home Orders and the Perceived Impact of COVID-19 on Daily Life. Psychiatry Res. 2020, 289, 113098. [Google Scholar] [CrossRef]
  124. Rosa, J.; Flores, N. Unsettling race and language: Toward a sociolinguistic perspective. Lang. Soc. 2017, 46, 621–647. [Google Scholar] [CrossRef] [Green Version]
  125. Blustein, D.L. The Importance of Work in an Age of Uncertainty: The Eroding Work Experience in America; Oxford University Press: Oxford, UK, 2019. [Google Scholar]
  126. Asperholm, M.; Högman, N.; Rafi, J.; Herlitz, A. What did you do yesterday? A meta-analysis of sex differences in episodic memory. Psychol. Bull. 2019, 145, 785–821. [Google Scholar] [CrossRef]
  127. Liu, X.; Bowe, S.J.; Li, L.; Too, L.S.; Lamontagne, A.D. Psychosocial job characteristics and mental health: Do associations differ by migrant status in an Australian working population sample? PLoS ONE 2020, 15, e0242906. [Google Scholar] [CrossRef]
  128. Aguiar-Quintana, T.; Nguyen, T.H.H.; Araujo-Cabrera, Y.; Sanabria-Díaz, J.M. Do job insecurity, anxiety and depression caused by the COVID-19 pandemic influence hotel employees’ self-rated task performance? The moderating role of employee resilience. Int. J. Hosp. Manag. 2021, 94, 102868. [Google Scholar] [CrossRef]
  129. Blakey, S.M.; Abramowitz, J.S. The effects of safety behaviors during exposure therapy for anxiety: Critical analysis from an inhibitory learning perspective. Clin. Psychol. Rev. 2016, 49, 1–15. [Google Scholar] [CrossRef] [PubMed]
  130. Wheaton, M.G.; Messner, G.R.; Marks, J.B. Intolerance of uncertainty as a factor linking obsessive-compulsive symptoms, health anxiety and concerns about the spread of the novel coronavirus (COVID-19) in the United States. J. Obsessive-Compulsive Relat. Disord. 2021, 28, 100605. [Google Scholar] [CrossRef] [PubMed]
  131. Ng, Y.J.; Yang, J.; Vishwanath, A. To fear or not to fear? Applying the social amplification of risk framework on two environmental health risks in Singapore. J. Risk Res. 2018, 21, 1487–1501. [Google Scholar] [CrossRef]
  132. Radloff, L.S. The CES-D scale: A self-report depression scale for research in the general population. Appl. Psycho-Log. Meas. 1977, 1, 385–401. [Google Scholar] [CrossRef]
  133. Wang, G.; Wei, Y.; Qiao, S. Generalized Inverses: Theory and Computations; Springer: Berlin/Heidelberg, Germany, 2018; Volume 53. [Google Scholar]
  134. Goodhue, D.L.; Lewis, W.; Thompson, R. Does PLS have advantages for small sample size or non-normal data? MIS Q. 2012, 36, 981–1001. [Google Scholar] [CrossRef] [Green Version]
  135. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  136. Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  137. Obrenovic, B.; Du, J.; Godinic, D.; Baslom, M.M.M.; Tsoy, D. The Threat of COVID-19 and Job Insecurity Impact on Depression and Anxiety: An Empirical Study in the USA. Front. Psychol. 2021, 12, 3162. [Google Scholar] [CrossRef] [PubMed]
  138. Aguilar Quintana, D.; Velarde Mendívil, A.T.; Camarena Gómez, D.M.J. Distribución comercial de una sopa tradicional con innovación. Vértice Univ. 2021, 23, 3–14. [Google Scholar] [CrossRef]
Sustainability 15 05312 g001 550
Figure 1. Conceptual research model.
Figure 1. Conceptual research model.
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Figure 2. Measurement model (CFA).
Figure 2. Measurement model (CFA).
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Figure 3. Structural model.
Figure 3. Structural model.
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Figure 4. Improved structural model.
Figure 4. Improved structural model.
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Table
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
Social.MediaFear.CovidAnxietyDepression
N327
Mean3.30192.53042.09951.8758
Standard Error of Mean0.066350.024920.058230.05448
Median3.50362.67701.78641.4908
Mode0.98 a0.72 a1.12 a1.07 a
Standard Deviation1.199880.450711.053060.98511
Variance1.4400.2031.1090.970
Range3.882.424.284.21
Minimum0.980.611.121.07
Maximum4.863.045.415.28
Percentiles252.60732.31931.28101.1453
503.50362.67701.78641.4908
754.37982.86252.30342.1620
a. Multiple modes exist. The smallest value is shown.
Table
Table 2. Discriminant, convergent valdity and multicollinearity.
Table 2. Discriminant, convergent valdity and multicollinearity.
ToleranceIFRVESVMaxR(H)Fear CovidDepressAnxietySocial Media
Fear.Covid0.9671.0370.9060.5220.0490.9190.722
Depress0.9461.0570.9070.620.6120.9250.1160.787
Anxiety 0.9430.7050.6120.950.2210.7820.839
Social
Media		0.9651.0370.9430.7690.0340.9450.0790.1190.1840.877
Table
Table 3. Model fit indices for the confirmatory factor analysis.
Table 3. Model fit indices for the confirmatory factor analysis.
MeasureEstimateThreshold
CMIN3496.781–
DF948–
CMIN/DF3.689Between 1 and 3
CFI0.879>0.95
SRMR0.064<0.08
RMSEA0.052<0.06
Pclose0.028>0.05
Table
Table 4. Model fit indices for the structural equation model.
Table 4. Model fit indices for the structural equation model.
MeasureEstimateThreshold
CMIN15.92–
DF3–
CMIN/DF5.307Between 1 and 3
CFI0.99>0.95
SRMR0.06<0.08
RMSEA0.066<0.06
Pclose0.168>0.05
Table
Table 5. Standardized parameter estimates, standard errors, and p values for the structural model.
Table 5. Standardized parameter estimates, standard errors, and p values for the structural model.
Unstandardized EstimateStandardized EstimateSECRp
Fear.Covid←Social.Media0.0370.0970.0211.763p < 0.1
Anxiety←Fear.Covid0.5130.2220.1244.14p < 0.01
Depress←Fear.Covid0.3710.170.1193.106p < 0.01
Anxiety←Social.Media0.0810.0940.0253.287p < 0.05
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Gu, X.; Obrenovic, B.; Fu, W. Empirical Study on Social Media Exposure and Fear as Drivers of Anxiety and Depression during the COVID-19 Pandemic. Sustainability 2023, 15, 5312. https://doi.org/10.3390/su15065312

AMA Style

Gu X, Obrenovic B, Fu W. Empirical Study on Social Media Exposure and Fear as Drivers of Anxiety and Depression during the COVID-19 Pandemic. Sustainability. 2023; 15(6):5312. https://doi.org/10.3390/su15065312

Chicago/Turabian Style

Gu, Xiao, Bojan Obrenovic, and Wei Fu. 2023. "Empirical Study on Social Media Exposure and Fear as Drivers of Anxiety and Depression during the COVID-19 Pandemic" Sustainability 15, no. 6: 5312. https://doi.org/10.3390/su15065312

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