Spread mechanism and control strategy of social network rumors under the influence of COVID-19

Scenario-based assessment of emergency management of urban infectious disease outbreaks

Infectious diseases pose a severe threat to human health and are accompanied by significant economic losses. Studies of urban outbreaks of infectious diseases are diverse. However, previous studies have neglected the identification of critical events and the evaluation of scenario-based modeling of urban infectious disease outbreak emergency management mechanisms. In this paper, we aim to conduct an empirical analysis and scenario extrapolation using a questionnaire survey of 18 experts, based on the CIA-ISM method and scenario theory, to identify the key factors influencing urban infectious disease outbreaks. Subsequently, we evaluate the effectiveness of urban infectious disease outbreak emergency management mechanisms. Finally, we compare and verify the actual situation of COVID-19 in China, drawing the following conclusions and recommendations. (1) The scenario-based urban infectious disease emergency management model can effectively replicate the development of urban infectious diseases. (2) The establishment of an emergency command center and the isolation and observation of individuals exposed to infectious diseases are crucial factors in the emergency management of urban outbreaks of infectious disease.

A Serious Game ("Fight With Virus") for Preventing COVID-19 Health Rumors: Development and Experimental Study

BACKGROUND

Health rumors arbitrarily spread in mainstream social media on the internet. Health rumors emerged in China during the outbreak of COVID-19 in early 2020. Many midelders/elders (age over 40 years) who lived in Wuhan believed these rumors.

OBJECTIVE

This study focused on designing a serious game as an experimental program to prevent and control health rumors. The focus of the study was explicitly on the context of the social networking service for midelders/elders.

METHODS

This research involved 2 major parts: adopting the Transmission Control Protocol model for games and then, based on the model, designing a game named "Fight With Virus" as an experimental platform and developing a cognitive questionnaire with a 5-point Likert scale. The relevant variables for this experimental study were defined, and 10 hypotheses were proposed and tested with an empirical study. In total, 200 participants were selected for the experiments. By collecting relevant data in the experiments, we conducted statistical observations and comparative analysis to test whether the experimental hypotheses could be proved.

RESULTS

We noted that compared to traditional media, serious games are more capable of inspiring interest in research participants toward their understanding of the knowledge and learning of health commonsense. In judging and recognizing the COVID-19 health rumor, the test group that used game education had a stronger ability regarding identification of the rumor and a higher accuracy rate of identification. Results showed that the more educated midelders/elders are, the more effective they are at using serious games.

CONCLUSIONS

Compared to traditional media, serious games can effectively improve midelders'/elders' cognitive abilities while they face a health rumor. The gameplay effect is related to the individual's age and educational background, while income and gender have no impact.

What Drives People to Share Misinformation on Social Media during the COVID-19 Pandemic: A Stimulus-Organism-Response Perspective

(1) Background: Misinformation is prevalent on social media in the age of COVID-19, exacerbating the threat of the pandemic. Uncovering the processes underlying people's misinformation sharing using social media assists people to cope with misinformation during the COVID-19 pandemic. This study extends the stimulus-organism-response framework to examine how individuals' social media dependency relates to their misinformation sharing behavior, with a focus on the underlying processes. (2) Methods: A total of 393 valid questionnaires were collected using a survey method to test the proposed research model. (3) Results: The results demonstrate that informational dependency and social dependency engender both positive and negative cognitive states, namely perceived information timeliness, perceived socialization and social overload, which then invoke positive as well as negative affect. What is more, the results show that both positive affect and negative affect can engender misinformation sharing. (4) Conclusions: Theoretically, this study uncovers the processes that lead to misinformation sharing on social media during the COVID-19 pandemic. Practically, this study provides actionable guidelines on how to manage social media usage and social media content to cope with misinformation sharing during the pandemic.

An entropy-based method to control COVID-19 rumors in online social networks using opinion leaders

- In the ongoing COVID-19 pandemic, people spread various COVID-19-related rumors and hoaxes that negatively influence human civilization through online social networks (OSN). The proposed research addresses the unique and innovative approach to controlling COVID-19 rumors through the power of opinion leaders (OLs) in OSN. The entire process is partitioned into two phases; the first phase describes the novel Reputation-based Opinion Leader Identification (ROLI) algorithm, including a unique voting method to identify the top-T OLs in the OSN. The second phase describes the technique to measure the aggregated polarity score of each posted tweet/post and compute each user's reputation. The empirical reputation is utilized to calculate the user's trust, the post's entropy, and its veracity. If the experimental entropy of the post is lower than the empirical threshold value, the post is likely to be categorized as a rumor. The proposed approach operated on Twitter, Instagram, and Reddit social networks for validation. The ROLI algorithm provides 91% accuracy, 93% precision, 95% recall, and 94% F1-score over other Social Network Analysis (SNA) measures to find OLs in OSN. Moreover, the proposed approach's rumor controlling effectiveness and efficiency is also estimated based on three standard metrics; affected degree, represser degree, and diffuser degree, and obtained 26%, 22%, and 23% improvement, respectively. The concluding outcomes illustrate that the influence of OLs is exceptionally significant in controlling COVID-19 rumors.

New Insights Into the Social Rumor Characteristics During the COVID-19 Pandemic in China

Background

In the early stage of the COVID-19 outbreak in China, several social rumors in the form of false news, conspiracy theories, and magical cures had ever been shared and spread among the general public at an alarming rate, causing public panic and increasing the complexity and difficulty of social management. Therefore, this study aims to reveal the characteristics and the driving factors of the social rumors during the COVID-19 pandemic.

Methods

Based on a sample of 1,537 rumors collected from Sina Weibo's debunking account, this paper first divided the sample into four categories and calculated the risk level of all kinds of rumors. Then, time evolution analysis and correlation analysis were adopted to study the time evolution characteristics and the spatial and temporal correlation characteristics of the rumors, and the four stages of development were also divided according to the number of rumors. Besides, to extract the key driving factors from 15 rumor-driving factors, the social network analysis method was used to investigate the driver-driver 1-mode network characteristics, the generation driver-rumor 2-mode network characteristics, and the spreading driver-rumor 2-mode characteristics.

Results

Research findings showed that the number of rumors related to COVID-19 were gradually decreased as the outbreak was brought under control, which proved the importance of epidemic prevention and control to maintain social stability. Combining the number and risk perception levels of the four types of rumors, it could be concluded that the Creating Panic-type rumors were the most harmful to society. The results of rumor drivers indicated that panic psychology and the lag in releasing government information played an essential role in driving the generation and spread of rumors. The public's low scientific literacy and difficulty in discerning highly confusing rumors encouraged them to participate in spreading rumors.

Conclusion

The study revealed the mechanism of rumors. In addition, studies involving rumors on different emergencies and social platforms are warranted to enrich the findings.

A probabilistic approach toward evaluation of Internet rumor on COVID

Several people around the world have died from the coronavirus (COVID-19) disease. With the increase in COVID-19 cases, distribution, and deaths, much has occurred regarding the ban on travel, border closure, curfews, and the disturbance in the supply of services and goods. The world economy was severely affected by the spread of the virus. Every day, new discussions and debates started, and more people were in fear. Occasionally, unconfirmed information is shared on social media sites as if it were accurate information. Sometimes, it becomes viral and disturbs people's emotions and beliefs. Fake news and rumors are widespread forms of unconfirmed and false information. This type of news should be tracked speedily to prevent its negative impact on society. An ideal system is the dire need of modern-day society to evaluate the Internet rumors on COVID. Therefore, the current study has considered a probabilistic approach for evaluating the Internet rumors about COVID. The fuzzy logic tool in MATLAB was used for experimental and simulation purposes. The results revealed the effectiveness of the proposed work.

Factors influencing fake news rebuttal acceptance during the COVID-19 pandemic and the moderating effect of cognitive ability

Fake news is spreading rapidly on social media and poses a serious threat to the COVID-19 outbreak response. This study thus aims to reveal the factors influencing the acceptance of fake news rebuttals on Sina Weibo. Drawing on the elaboration likelihood model (ELM), we used text mining and the econometrics method to investigate the relationships among the central route (rebuttal's information readability and argument quality), peripheral route (rebuttal's source credibility, including authority and influence), and rebuttal acceptance, as well as the moderating effect of receiver's cognitive ability on these relationships. Our findings suggest that source authority had a negative effect on rebuttal acceptance, while source influence had a positive effect. Second, both information readability and argument quality had positive effects on rebuttal acceptance. In addition, individuals with low cognitive abilities relied more on source credibility and argument quality to accept rebuttals, while individuals with high cognitive abilities relied more on information readability. This study can provide decision support for practitioners to establish more effective fake news rebuttal strategies; it is especially valuable to reduce the negative impact of fake news related to major public health emergencies and safeguard the implementation of anti-epidemic strategies.

Evaluating Rumor Debunking Effectiveness During the COVID-19 Pandemic Crisis: Utilizing User Stance in Comments on Sina Weibo

Background: The spread of rumors related to COVID-19 on social media has posed substantial challenges to public health governance, and thus exposing rumors and curbing their spread quickly and effectively has become an urgent task. This study aimed to assist in formulating effective strategies to debunk rumors and curb their spread on social media.

Methods: A total of 2,053 original postings and 100,348 comments that replied to the postings of five false rumors related to COVID-19 (dated from January 20, 2020, to June 28, 2020) belonging to three categories, authoritative, social, and political, on Sina Weibo in China were randomly selected. To study the effectiveness of different debunking methods, a new annotation scheme was proposed that divides debunking methods into six categories: denial, further fact-checking, refutation, person response, organization response, and combination methods. Text classifiers using deep learning methods were built to automatically identify four user stances in comments that replied to debunking postings: supporting, denying, querying, and commenting stances. Then, based on stance responses, a debunking effectiveness index (DEI) was developed to measure the effectiveness of different debunking methods.

Results: The refutation method with cited evidence has the best debunking effect, whether used alone or in combination with other debunking methods. For the social category of Car rumor and political category of Russia rumor, using the refutation method alone can achieve the optimal debunking effect. For authoritative rumors, a combination method has the optimal debunking effect, but the most effective combination method requires avoiding the use of a combination of a debunking method where the person or organization defamed by the authoritative rumor responds personally and the refutation method.

Conclusion: The findings provide relevant insights into ways to debunk rumors effectively, support crisis management of false information, and take necessary actions in response to rumors amid public health emergencies.

The causes, impacts and countermeasures of COVID-19 "Infodemic": A systematic review using narrative synthesis

An unprecedented infodemic has been witnessed to create massive damage to human society. However, it was not thoroughly investigated. This systematic review aims to (1) synthesize the existing literature on the causes and impacts of COVID-19 infodemic; (2) summarize the proposed strategies to fight with COVID-19 infodemic; and (3) identify the directions for future research. A systematic literature search following the PRISMA guideline covering 12 scholarly databases was conducted to retrieve various types of peer-reviewed articles that reported causes, impacts, or countermeasures of the infodemic. Empirical studies were assessed for risk of bias using the Mixed-Methods Appraisal Tool. A coding theme was iteratively developed to categorize the causes, impacts, and countermeasures found from the included studies. Social media usage, low level of health/eHealth literacy, and fast publication process and preprint service are identified as the major causes of the infodemic. Besides, the vicious circle of human rumor-spreading behavior and the psychological issues from the public (e.g., anxiety, distress, fear) emerges as the characteristic of the infodemic. Comprehensive lists of countermeasures are summarized from different perspectives, among which risk communication and consumer health information need/seeking are of particular importance. Theoretical and practical implications are discussed and future research directions are suggested.

Spread Mechanism and Control Strategies of Rumor Propagation Model Considering Rumor Refutation and Information Feedback in Emergency Management

The rumor-free equilibrium state and rumor-endemic equilibrium state are two symmetric descriptions of the status of a system. The constant spreading of rumors would affect the smooth operation of emergency management procedures and cause unnecessary social and economic loss. To reduce the negative effect of rumor propagation, in this paper, we introduce a compartmental model of rumor propagation, which considers the rumor refutation of public and information feedback. By deriving mean-field equations that describe the dynamics of the model, we use analytical and numerical solutions of these equations to investigate the threshold and dynamics of the model in both the closed system and open system. The results imply that the initial equilibrium point is not stable and there exists a rumor-free equilibrium point; in the open system, there exists a threshold beyond which rumors can spread; the stability of the initial equilibrium point is related to the threshold R0 = (φ*α)/μ, and there exists a rumor-endemic equilibrium point. The development process of rumor propagation can be divided into four stages: latent period, progressive period, intense period, and recession period. Under the influence of population, rumor spreading can exceed the threshold readily because the migration rate μ is usually less than the proportion of ignorants without critical ability φ, and the rumor spreading process in an open system presents a fluctuating development, the rumor would not disappear in this autonomous system. Based on the analysis, we propose some measures, such as providing open and efficient information queries and exchange platforms, etc.

Factors affecting individual online rumor sharing behavior in the COVID-19 pandemic

With the outbreak of COVID-19, online sharing of rumors about the disease is of growing concern worldwide. Drawing on the stimulus–organism–response (S–O-R) framework, this study aims to explore the impacts of peer condition and peer communication on fear of COVID-19, and the impact of fear of COVID-19 on online rumor sharing behavior, by considering the contingency effect of health self-efficacy. Data from 1167 respondents to an online survey in China were adopted to test our research model. The results indicate that peer communication and peer condition induce fear of COVID-19, and fear of COVID-19 results in online rumor sharing. Fear of COVID-19 mediates the effects of peer communication and peer condition on online rumor sharing. Health self-efficacy alleviates the positive effect of peer communication on fear of COVID-19, and the positive effect of fear of COVID-19 on online rumor sharing. This study advances the literature on online rumor sharing and S–O-R, and provides practical implications to social media users and governments.

Stability and optimal control strategies for a novel epidemic model of COVID-19

In this paper, a novel two-stage epidemic model with a dynamic control strategy is proposed to describe the spread of Corona Virus Disease 2019 (COVID-19) in China. Combined with local epidemic control policies, an epidemic model with a traceability process is established. We aim to investigate the appropriate control strategies to minimize the control cost and ensure the normal operation of society under the premise of containing the epidemic. This work mainly includes: (i) propose the concept about the first and the second waves of COVID-19, as well as study the case data and regularity of four cities; (ii) derive the existence and stability of the equilibrium, the parameter sensitivity of the model, and the existence of the optimal control strategy; (iii) carry out the numerical simulation associated with the theoretical results and construct a dynamic control strategy and verify its feasibility.