Excessive social media use during the rainstorm disaster: Exploring the effects of social media overload on anxiety

doi:10.21203/rs.3.rs-3234361/v1

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Excessive social media use during the rainstorm disaster: Exploring the effects of social media overload on anxiety

https://doi.org/10.21203/rs.3.rs-3234361/v1

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This paper studies the adverse effects and potential mechanisms of social media overload in a rainstorm environment. This paper takes the stressor-strain-outcome (SSO) as the theoretical basis to build a model, which mainly reflects social media overload (information overload, communication overload and social overload), social media fatigue and disaster anxiety. The partial least square structural equation modeling (PLS-SEM) method was used to collect 547 valid samples for the study. The final results show that social media fatigue is caused by social media overload, while disaster anxiety is formed by social media fatigue. Social media fatigue partially mediates communication overload and disaster anxiety. This study advances the literature on social media adverse effects research and SSO and provides practical implications to social media users and risk management departments.

social media overload

social media fatigue

disaster anxiety

rainstorm disaster

SSO

As the frequency and impact of natural disasters are increasing overtime, disasters harasses social order, interfere with people's lives, damage infrastructure and dismay ecosystems which leads to it causing a significant impact to human security (1, 2). A natural disaster is a traumatic event with a broad impact (3). It has the potential to trigger fear for personal safety in those who experience the disaster (4). Therefore, natural disasters are associated with widespread negative psychological and physical health consequences. Researchers are concerned about the impact of natural disasters on people's psychological conditions, such as psychological distress and psychiatric disorders (5). People are prone to anxiety when facing difficult situations or threats caused by disasters (6). Anxiety is also a central problem which people need to overcome in response to disasters (4). Research has found that disaster-induced anxiety problems are more common than depression or PSTD (post-traumatic stress disorder) (7). Anxiety impairs the mental health of people who are exposed to disasters (6). A previous study founds that anxiety leads to cognitive bias, which will interrupt people’s judgment and affect their crisis response (8). When studying the impact of anxiety on disaster prevention behavior, Wirtz et al. found that high level of anxiety can lead to a decrease in individual preparedness behaviors to deal with disasters (9). Anxiety makes people think they are incapable of responding to threats and even leads them to give up taking appropriate responses (10).

Social media has become an essential tool for dealing with natural disasters (11). It plays a vital role in helping people cope with the threat of disaster events in the following aspects: (1) information transmission, (2) early preparedness and warning for emergency and crises, (3) environmental importance and healthy development, (4) weighing public oo hparticipation at the disaster and concern level (12, 13). Scholars emphasize that despite the important role played by social media in disaster responses, its negative consequences cannot be ignored (14, 15). Social media overload is a typical adverse consequence of overuse of social media (16). Overload refers to an individual’s subjective perception and evaluation of the amount of information, people or objects that exceeds their own processing capacity (17). Social media overload refers to users’ overload experience in using social media (18). Some scholars who had conducted researches in terms of the impact social media overload found that social media overload harm people's psychology and behavior (18). Some scholars found that the overload experience caused by excessive use of social media and exposure to the online news during the COVID-19 exacerbated people's anxiety about the epidemic (19–21). Murthy and Gross found that the public was more anxious due to excessive exposure to false information and rumors on social media (22). The World Health Organization reported that information overload was associated with general anxiety among individuals during the COVID-19 pandemic (23, 24). Bala et al. and Garfin et al. found that excessive use of social media led to overload, which exacerbated users’ panic about crises and even led to wrong responses (25, 26). Hughes et al., Kumar et al. and Carr found that the consequence of social media overload was that users were difficult to distinguish, judge and screen valuable information, which, therefore, made it more complicated for users to effectively utilize the platform to cope with crises and maybe make emergency managers too confused to cope with the public's response to a disaster (27–29).

In the current relevant studies, significant findings have been made on the relationship between social media overload and anxiety, but the relationship between the two sides is not clear from the perspective of natural disasters (rainstorm, typhoon, etc.). The existing research conclusions are mainly based on public health emergencies such as the COVID-19 pandemic. Therefore, it is difficult to explain why there is a significant tie between social media overload and anxiety in rainstorm disasters. The lack of research in this aspect is also not conducive to the disaster management departments using social media effectively for risk communication or disaster relief activities in sudden rainstorm disasters. Therefore, this study is based on the rainstorm scenario to research the following two issues: (1) Does Social Media Overload Affect Users' Disaster Anxiety? (2) What Is the Impact Mechanism of Social Media Overload on Users’ Disaster Anxiety?

The achievements of this research can be obtained from three aspects. First, it solidifies the impact of social media overload on viewers' anxiety in heavy rainstorms. Second, the research on the adverse effects of social media is further explored and extended the application of the SSO theoretical framework. Lastly, the research provides a clear reference for disaster response departments to improve public-targeted risk communication strategies during sudden rainstorm disasters.

2.1. Model Design

Koeske and Koeske proposed a theoretical framework in 1993, namely, Stressor-Strain-Outcome (SSO), which consists of three parts: stressor, the environmental factors that cause stress and affect individual psychological state; strain, an individual’s subjective psychological experience caused by the stressor including perceptions, feelings and thoughts; outcome, the individual's response to stress, including psychological or behavioral responses (30). According to the SSO theoretical framework, stressor induces STRAIN, leading to specific outcomes. Furthermore, strain is the mediating factor between stressor and outcome. Stressor does not directly lead to a specific outcome, but indirectly through the mediation of strain (30). The SSO theoretical framework is widely used to study the pressures associated with technology use and their consequences (31). In this study, social media overload can be seen as a stressor. Social media fatigue can be regarded as an individual's strain caused by a stressor and disaster anxiety is the outcome.

2.2. STRESSOR——Social Media Overload

People experiencing sudden disasters use social media significantly longer and more often (32), which can easily lead to social media overload (16). Social media overload usually includes three aspects: information overload, communication overload and social overload (18). Information overload indicates that information recipients receive far more information than they can handle (15). Communication overload mainly refers to the fact that an individual can conduct limited communication behavior (33). Social overload mainly refers to when the amount of social relationship is on the rise, the user's social state tends to be tired. This state will provide more social support to online contacts (34).

2.3. STRAIN——Social Media Fatigue

Social media fatigue is a non-objective fatigue that can result from intemperate use (35). It is a subjective, multidimensional psychological state that includes feelings of tiredness, annoyance, anger, disappointment, loss of interest, as well as the decreased needs or motivations related to all aspects of social media use and interaction (35). People's long-term and high-frequency use of social media will continue to consume their cognitive energy, which may further lead to social media fatigue (36). People experience different social media fatigue intensity under the same circumstances (37).

2.4. OUTCOME——Disaster Anxiety

For the understanding of anxiety, scholars believe that "mainly through an unsatisfactory emotion. It reflects negative emotions such as anxiety and panic" (38). The appearance of anxiety is associated with mental blows and upcoming situations that may pose threats or dangers. Its subjective expression is individuals being nervous, unpleasant, and even too painful to control themselves (39). Anxiety reflects people’s fears about difficult situations or threats in the future (40). Previous research found that people's maladaptive response to disasters would lead to disaster anxiety (6).

2.5 Research Hypothesis

2.5.1. The relationship between social media overload and social media fatigue

Social media overload will have a negative impact on users, making their psychological state tend to develop in a negative direction (41). Social media overload can lead to social media fatigue (42). Social media overload is mainly manifested in three aspects, which are social overload, information and communication overload (18).

When people are experiencing disasters, they will continue to search, receive, identify, read and process the constant inflow of information on social media, resulting in the consumption of a lot of personal time and energy and the following sense of information overload (16). Studies reveal that information overload will cause users' social media fatigue (37, 42). Therefore, this study proposes the following hypothesis:

H1a: Information overload is positively associated with social media fatigue.

People experiencing natural disasters would be more often to interact with their families, friends and communities than usual (43). By exchanging information they get with each other, they aim to relieve the feeling of uncertainty (44). Communication overload occurs when other users engage their attention and interfere with what they are currently doing (18). Communication overload and social media fatigue can occur when the requests for conversation and communication in social media are not properly addressed. According to a large number of studies, social media fatigue is one of the major causes of communication overload (41). Therefore, this study proposes the following hypothesis:

H1b: Communication overload is positively associated with social media fatigue.

As a kind of current social norm, people gradually provide corresponding social needs to their friends through social media (34). However, when the requests are excessive and beyond the emotional cognitive range of users, users may feel overloaded resulting in social media fatigue (41). Studies have shown that social media fatigue is caused by communication overload (34, 45). Therefore, this study proposes the following hypothesis:

H1c: Social overload is positively associated with social media fatigue.

2.5.2. The relationship between social media overload and disaster anxiety

A previous study found that people's long-term and high-frequency use of social media will keep consuming their mental energy and cause social media fatigue (36). Excessive consumption of cognitive energy will make it difficult for users to distinguish, judge, and screen valuable information (28, 29, 42). Anxiety was related to irrational judgment and unsystematic information processing (46). Previous study found that social media fatigue can lead to depression and anxiety (47). Therefore, this study proposes the following hypothesis:

H2: Social media fatigue is positively associated with disaster anxiety.

2.5.3. The mediating role of social media fatigue

Social media fatigue acts as a bridge between social media overload and anxiety. The pieces of evidence are as follows. According to the principle of the SSO model, the external stimulus (stressor) usually triggers people’s strain response (strain), and the strain further causes the corresponding psychological or behavioral outcome. In other words, external stimuli (social media overload) through strain (social media fatigue) ultimately lead to corresponding outcomes (anxiety) (30). Second, too much information will cost users more time on social media and damage people's cognition, resulting in social media fatigue (37). Past research has shown that social media fatigue can lead to anxiety (47, 48). Therefore, according to the above analysis, the following assumptions are obtained:

H3a: Social media fatigue mediates the relationship between information overload and disaster anxiety.

H3b: Social media fatigue mediates the relationship between communication overload and disaster anxiety.

H3c: Social media fatigue mediates the relationship between social overload and disaster anxiety.

2.6 Research Framework

According to the above information, social media fatigue is caused by social media overload. The further deepening of social media fatigue will lead to anxiety, and social media fatigue plays a role in bridging the gap between media overload and disaster anxiety.

In summary, the theoretical framework of this study can be obtained, as shown in Fig. 1.

3.1. Measures

The questionnaire can be divided into two parts. (1) The essential personal information items (including age, gender, education and occupation). (2) As the variable measurement part, mainly includes three aspects: social media overload, social media fatigue and disaster anxiety. The questionnaire items used to measure variables in this study are all from existing studies. On social media overload, the measurement of information overload uses the scale of the research by Chen et al (49), the measurement of communication overload uses the scale of the research by Karr-Wisniewski and Lu (45), and the measurement of social overload uses the scale of the research by Maier et al (34). The measurement of social media fatigue uses the research scale by Lee et al (42). The measurement of disaster anxiety uses the scale of the research by Brooks and Schweitzer (50). Each variable measurement item is measured according to the Likert scale (From 1="strongly disagree" to 5="strongly agree").

We invite an English expert to translate the English version into Chinese. Moreover, we enlist the help of a risk communication research expert and a communications expert to improve the questionnaire questions according to the rainstorm situation to ensure that the questionnaire is easy to understand for respondents. In order to make the formal survey more effective, this study carries out a small-scale preliminary survey and distributes 20 preliminary survey questionnaires (the respondents include teachers, college students, corporate employees, and government workers). According to the preliminary survey data, the Cronbach's α values of all variables are higher than the threshold value of 0.6. The data obtained from the pre-survey is not used in the final data analysis.

3.2. Data Collection

This study is carried out utilizing an online questionnaire survey. First, enter the questionnaire into the professional survey website (https://www.wenjuan.com/) to get the questionnaire link. Then, share the questionnaire with people living in Henan on social media through the interpersonal network of relatives and friends. Third, invite those who have experienced rainstorms lasting for more than 24 hours in the past month to fill in the questionnaire. The survey period is from August 10 to 30, 2021. In the end, there are a total of 789 questionnaires. According to the rules for users of https://www.wenjuan.com/, this study do not have any participants below 16 years of age. Excluding invalid questionnaires with obviously contradictory answers within 30 seconds of filling time, we get 547 valid questionnaires. Of all respondents, 55.21% were female and 44.79% were male. The age group between 18-29 years old accounted for 69.29% of the respondents; 30-39 years old accounted for 23.03%, 40-49 years old accounted for 5.12%, under 18 years old accounted for 1.46% of the total number of respondents, and the smallest proportion of people over 50 years old was 1.10%. More than half of the respondents had a university or college degree, accounting for 56.31% of the total number of respondents, followed by 36.93% with a postgraduate degree and 6.76% with a high school (including secondary school or technical) degree or less. In terms of the distribution of respondents' positions, students occupied the largest group, accounting for 34.37%; of the remaining positions 33.82% are staff; 17% are government employees; 4.75% are freelancers; 2.56% are unemployed and the remaining 7.5% are in other occupations. The distribution of the participants is presented in Table 1.

Table 1. Demographic profile of participants (n = 547).

Characteristic		Count	%
Gender	Male	245	44.79
Gender	Female	302	55.21
Age	under18	8	1.46
	18-29	379	69.29
	30-39	126	23.03
	40-49	28	5.12
	50 and above	6	1.10
Education	Senior high school or equivalent	37	6.76
	Undergraduate and College Degree	308	56.31
	Postgraduate	202	36.93
Occupation	Student	188	34.37
	Government employee	93	17.00
	Staff	185	33.82
	Freelancer	26	4.75
	Unemployed	14	2.56
	Others	41	7.50

3.3. Data Analysis

This study uses the Partial Least Squares Structural Equation Modeling (PLS-SEM) method for data analysis and model building (51). The reason is that PLS-SEM method is suitable for exploratory research and small-sample research. The analysis software is SmartPLS 3.3.9.

3.3.1 Measurement Model

The part of measurement model testing is mainly about testing the reliability and validity (convergent and discriminant validity).

Reliability analysis adopts Cronbach’s α and Composite Reliability (CR). The Cronbach's α of all variables in this study are between 0.770 and 0.903, which are all higher than the threshold value of 0.6 (51). Moreover, all of the CR is between 0.866 and 0.936, higher than the threshold value of 0.7. It shows that each variable has good composite reliability and internal consistency reliability (51). See Table 2 for details.

Table 2. The reliability and convergent validity of the constructs.

Constructs	Items		Loading	α	CR	AVE
Information overload		IO1: I am often distracted by the amount of information on multiple channels/sources about the rainstorm.	0.812	0.770	0.866	0.683
		IO2: I am not certain that the information about the rainstorm on social media fits my needs well to make better decisions	0.789
		IO3: I am overwhelmed by the amount of information that I process these days about the rainstorm.	0.876
Communication overload		CO1: I feel like I have to send many more messages to friends through SNS than I would want to send.	0.887	0.863	0.915	0.782
		CO2: I receive too many messages from friends (or acquaintances) through SNS.	0.893
		CO3: I often feel overloaded with communication from SNS.	0.872
Social overload		SO1: I take too much care of the well-being of my friends on social media.	0.901	0.897	0.936	0.829
		SO2: I deal with my friends' problems on social media too much.	0.921
		SO3: I care for my friends on social media too often.	0.909
Social media fatigue		SMF1: I find it difficult to relax after continually using social media.	0.855	0.903	0.932	0.774
		SMF2: After a session of using social media, I feel really fatigued.	0.893
		SMF3: Due to using social media, I feel rather exhausted.	0.897
		SMF4: During social media use, I often feel too fatigued to perform other tasks well.	0.873
Disaster anxiety		DA1: How nervous did you feel while facing the related information about rainstorm on social media?	0.887	0.884	0.920	0.743
		DA2: How anxious did you feel while facing the related information about rainstorm on social media?	0.792
		DA3: How apprehensive did you feel while facing the related information about rainstorm on social media?	0.894
		DA4: How worried did you feel while facing the related information about rainstorm on social media?	0.871

1 Note: α, Cronbach's α; CR, composite reliability; AVE, average variance extracted.

Convergent validity is assessed by examining the Average Variance Extracted (AVE) and the Factor Loadings (52). As shown in Table 2, the AVE values of each variable are between 0.683 and 0.829, all higher than the threshold value of 0.5. Furthermore, the Factor Loadings values of each factor are more significant than the threshold value of 0.7, indicating good convergence validity.

Discriminant validity is the Fornell-Larcker metric (51) and the Heterotrait-Monotrait Ratio of Correlations (HTMT) for evaluation (53). Based on Fornell-Larcker’s measure, the square root of the AVE of a variable should be greater than the correlation coefficient between the variable and other variables, and each variable’s discriminant validity is satisfactory. See Table 3 for details. The HTMT value among all variables is between 0.38 and 0.612, as shown in the parentheses in Table 3, which are all less than the threshold value of 0.85, thus the discrimination validity is gratifying.

Table 3. Discriminant validity of the constructs (Fornell-Larker criterion and HTMT criterion)

	CO	DA	IO	SMF	SO
CO	0.884
DA	0.411(0.460)	0.862
IO	0.501(0.601)	0.392(0.467)	0.826
SMF	0.553(0.383)	0.397(0.385)	0.448(0.490)	0.880
SO	0.567(0.611)	0.353(0.442)	0.512(0.527)	0.464(0.527)	0.910

1 Note: CO, communication overload; DA, disaster anxiety; IO, Information overload; SMF, social media fatigue; SO, social overload. 2 Note: Diagonal elements in bold are the square root of the AVE. The HTMT value is in parentheses.

3.3.2. Structural model

We run the PLS algorithm to generate path coefficient values (β Value) and model explanatory power (R²), then runs Bootstrapping method (subsamples=5000) in order to analyse the statistical significance and interpretation rate of the relationship among variables in the structural model. The structural model test results are shown in Figure 2. There is a positive relationship between information overload and social media fatigue (β= 0.170; P<0.001), the discussion of H1a is correct. There is a positive correlation between communication overload and social media fatigue (β= 0.374; P<0.001). Therefore, hypothesis H1b is true. And H1c is true because there is a positive relationship between social overload and social media fatigue (β= 0.165; P<0.01). There is a positive correlation between social media fatigue and disaster anxiety (β= 0.166; P<0.01), and H2 is well founded. The variance explanation rate of social media fatigue on information overload and communication overload. social overload is 37.8%, and the variance explanation rate of disaster anxiety on social media fatigue is 25.7%.

3.3.3. Mediation Effect Test

The indirect effect test uses the Bootstrap method (51). H3b (Indirect Effects =0.062**, t = 2.677, VAF=40.52%) is true, social media fatigue will play a partial mediating role between communication overload and disaster anxiety. H3a (Indirect Effects =0.028*, t = 2.370, VAF=14.58%) and H3c (Indirect Effects =0.027*, t = 2.115, VAF=12.11%) are groundless, the mediation role of social media fatigue in information overload, social overload and disaster anxiety is not significant.Table 4 shows the results of the mediation effect test.

Table 4. The results of the mediation effect analysis.

Hypotheses	Independent Variable	Intermediary Variable	Dependent Variable	Direct Effects (T)	Indirect Effects (T)	Total Effects	VAF	Results
H3a	IO	SMF	DA	0.164 * *（2.819）	0.028 *（2.370）	0.192	14.58%	Not Supported
H3b	CO			0.091 * *（1.558）	0.062 * *（2.677）	0.153	40.52%	Supported
H3c	SO			0.196 ns（2.665）	0.027 * （2.115）	0.223	12.11%	Not Supported

1 Note: IO, information overload; CO, communication overload; SO, social overload; SMF, social media fatigue; DA, disaster anxiety. 2 Note: *p<0.1，**p<0.01，***p<0.001，ns：Non-significant. 2 Note: NO Mediation: VAF<20%, Partial mediation:20%<VAF<80%, Full mediation:80%<VAF.

4.1. Key Findings

Based on the SSO model, this paper uses social media overload as the STRESSOR, social media fatigue as the STRAIN (mediating variable), and disaster anxiety as the OUTCOME to establish its research model. Given this, it reveals the impact of social media overload on the disaster anxiety of social media users in rainstorm disasters and its impact mechanism. Three valuable findings of this study yielded are as follows:

Firstly, this paper verifies the three hypotheses of H1(a, b, c) to determine that media fatigue is positively proportional to social media overload. Meanwhile, H1a also shows that there is a positive relationship between social media overload and social fatigue. That is to say, information overload is a significant predictor of social media fatigue. Pang and Liu et al. also obtained corresponding results (20, 21). In H1b, the same research result as Whelan et al. was also obtained, that is, communication overload in social media overload is also positively correlated with social media fatigue (54). That means communication overload is an important predictor of social media fatigue. H1C indicates that social overload and social media fatigue in social media also show a positive relationship, which is not different from the results of Maier et al (34). The above results indicate that during sudden rainstorm disasters, users are overwhelmed by a large number of disaster-related messages and social requests posted through social media. Their continuous processing of this information will lead to excessive consumption of their cognitive energy, resulting in social media fatigue.

Second of all, the valid hypothesis of H2 proves that social media fatigue is significantly positively correlated with disaster anxiety. It means social media fatigue is an important predictor of disaster anxiety. The results show that users' strained emotions in sudden disasters——social media fatigue may further trigger their anxiety about the disaster. Studies have shown that a typical manifestation of social media fatigue is the decline of people's information processing ability and cognitive ability, affecting users' ability to analyze and judge disaster information (28, 29, 42). Anxiety is associated with irrational judgments and unsystematic information processing (46). When users have social media fatigue, they will experience cognitive and emotional tension (35), leading to a decline in their cognitive ability and the following symptoms including depression, anxiety, negative responses and other conditions (47, 55). This result is consistent with Dhir et al (47).

Third, the hypothesis that H3b holds proves that social media fatigue plays a mediating role between communication overload and disaster anxiety. It means communication overload indirectly leads to disaster anxiety through social media fatigue. This result revealed that interpersonal communication in social media is the channel through which disaster anxiety spreads among the population. Hypothesis H3a and H3c are not supported. It means social media fatigue does not mediate between information overload, social overload and disaster anxiety. The result shows it is not statistically significant that information overload and social overload indirectly lead to users' disaster anxiety. It may be due to the following reasons: in the event of sudden disasters, Chinese people pay more attention to information released from traditional media or official sources, which is of high quality, professionalism, accuracy, and clarity (56). Disseminating information with accurate content and in a clear format during a disaster helps people avoid unnecessary anxiety (57).

4.2. Implications for research

4.2.1. Theoretical implications

Firstly, there are few scholars who exploring the impact of social media overload on users’ disaster anxiety in rainstorm disasters. This study reveals its impact mechanism, deepening our understanding of this phenomenon.

Secondly, it makes the SSO model more applicable and enriches the structural equation modeling framework for social media research.

Thirdly, this study extends the research on the adverse effects of social media on rainstorm disasters.

4.2.2. Practical implications

The findings are meaningful to disaster management departments, social media service operators and administrators, and users.

Firstly, the above parties should understand that excessive use of social media for disaster communication risks exacerbating disaster anxiety.

Secondly, content of released disaster information should avoid aggravating the psychological pressure of users, and control the amount and frequency of releasing it.

This research will also be affected and restricted in all aspects.

(1) This study is based on a rainstorm event and collects data through online questionnaires in China. This method can efficiently obtain data in disaster-affected areas.This research method also has shortcomings, such as affected by sample distribution, which may limit the generalizability of the research findings. Future studies can explore different types of disaster and different countries and regions, making the results more practical.

(2) All data are obtained by interviewees in the form of self-report questionnaires. Although this method is widely used in academia because it is efficient and economical, but such data may have subjective bias. In future further research, we should pay attention to the diversity of methods, which can be carried out by means of psychological experiments. At the same time, we should also carry out the research by using a variety of data to make the results more accurate.

(3) This is an exploratory study, so only social media fatigue, is considered as a psychological factor leading to users’ disaster anxiety. There may be other psychological factors (such as neuroticism, exhaustion, fear, etc.) that can play a similar mediating role. Future research may attempt to explore the role of these factors.

This paper studies the adverse effects and potential mechanisms of social media overload in a rainstorm environment. Social media fatigue is caused by social media overload, and disaster anxiety is caused by social media fatigue. Social media fatigue mediates communication overload and anxiety. The research in this paper deepens the literature in this field. And its conclusions can provide reference for disaster response departments to formulate public-targeted risk communication strategies in rainstorm disasters.

Ethics approval and consent to participate

The study was approved by Ethics Committee of Guangxi University (GXU-2021-1001), all methods were carried out in accordance with relevant guidelines and regulations. All respondents read the informed consent form about the purpose and content of the study and voluntary participation in the study before filling out the questionnaire. Informed consent was obtained from all the study participants.

Consent for publication

Not applicable

Data Availability

The questionnaire and data are available upon special request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Funding

This research received no external funding.

Author Contributions

Conceptualization, Kai Li; Data curation, Dingxi Li; Formal analysis, Dingxi Li; Investigation, Dingxi Li; Methodology, Kai Li; Resources, Kai Li; Supervision, Kai Li; Validation, Kai Li; Visualization, Dingxi Li; Writing – original draft, Dingxi Li; Writing – review & editing, Kai Li.

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No competing interests reported.

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Excessive social media use during the rainstorm disaster: Exploring the effects of social media overload on anxiety

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Abstract

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1. Introduction

2. Theory Background

2.1. Model Design

2.2. STRESSOR——Social Media Overload

2.3. STRAIN——Social Media Fatigue

2.4. OUTCOME——Disaster Anxiety

2.5 Research Hypothesis

2.5.1. The relationship between social media overload and social media fatigue

2.5.2. The relationship between social media overload and disaster anxiety

2.5.3. The mediating role of social media fatigue

2.6 Research Framework

3. Research Methods

4. Discussion

4.1. Key Findings

4.2. Implications for research

4.2.1. Theoretical implications

4.2.2. Practical implications

5. Limitations and future research

6. Conclusions

Declarations

References

Additional Declarations

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