Rumor response strategy and rumor behavioral intention: The mediating roles of anxiety on Chinese social media during COVID-19

While social media has grown in popularity in today's society, and has facilitated the dissemination of accurate and valuable information, it also raises the equally pressing concern of rampant proliferation of rumors and false news. The recent global outbreak of COVID-19 witnessed the explosion of fake and misleading health rumors in social media. Governments are tasked with providing the public with the right information to influence their behavior and engagement in emergency decision-making and optimally address the risks of rumor influence. Therefore, it is important to choose an appropriate response strategy in a rumor-induced health crisis. This study has two main objectives: to identify effective rumor response strategies by the government to stem the spread of rumor during a health crisis, and to examine the role of anxiety in this process using the Situational Crisis Communication Theory (SCCT). Online quasi-experimental data was collected from 245 Chinese participants who were exposed to a false social media rumor that potato chips could spread COVID-19 and were randomly assigned to one of three rumor response strategies (denial, refute or attack). According to the one-way ANOVA results, the effect of the refute response on rumor-related behavior is the most positive, whereas the effects of denial and attack are not significantly different. The results of the mediation model using PROCESS Macro reveal that anxiety partially mediates the relationship between rumor response strategies and rumor-related behaviors (rumor dissemination intentions and behavior intention to consume products);the refute strategy reduces public anxiety and has a positive effect on public behavioral intentions. This study is relevant to COVID-19 rumor research with regard to government rumor response strategies on social media using data-based descriptive and quantitative analysis. © SEARCH Journal 2023.

Understanding the role of social media usage and health self-efficacy in the processing of COVID-19 rumors: A SOR perspective.

Apart from the direct health and behavioral influence of the COVID-19 pandemic itself, COVID-19 rumors as an infodemic enormously amplified public anxiety and cause serious outcomes. Although factors influencing such rumors propagation have been widely studied by previous studies, the role of spatial factors (e.g., proximity to the pandemic) on individuals' response regarding COVID-19 rumors remain largely unexplored. Accordingly, this study, drawing on the stimulus-organism-response (SOR) framework, examined how proximity to the pandemic (stimulus) influences anxiety (organism), which in turn determines rumor beliefs and rumor outcomes (response). Further, the contingent role of social media usage and health self-efficacy were tested. The research model was tested using 1246 samples via an online survey during the COVID-19 pandemic in China. The results indicate that: (1)The proximity closer the public is to the pandemic, the higher their perceived anxiety; (2) Anxiety increases rumor beliefs, which is further positively associated rumor outcomes; (3) When the level of social media usage is high, the relationship between proximity to the pandemic and anxiety is strengthened; (4) When the level of health self-efficacy is high, the effect of anxiety on rumor beliefs is strengthened and the effect of rumor beliefs on rumor outcomes is also strengthened. This study provides a better understanding of the underlying mechanism of the propagation of COVID-19 rumors from a SOR perspective. Additionally, this paper is one of the first that proposes and empirically verifies the contingent role of social media usage and health self-efficacy on the SOR framework. The findings of study can assist the pandemic prevention department in to efficiently manage rumors with the aim of alleviating public anxiety and avoiding negative outcomes cause by rumors.

Understanding Rumor Sharing Behavior During COVID-19: The Dominant Motivation and the Potential Consequences for Life Satisfaction.

During the COVID-19 pandemic, rumors were shared widely and quickly, leading to unfortunate consequences. To explore the dominant motivation underlying such rumor sharing behavior and the potential consequences for sharers' life satisfaction, two studies were conducted. Study 1 was based on representative popular rumors that circulated throughout Chinese society during the pandemic to examine the dominant motivation underlying rumor sharing behavior. Study 2 employed a longitudinal design to further test the dominant motivation underlying rumor sharing behavior and its effects on life satisfaction. The results of these two studies generally supported our hypotheses that people chose to share rumors during the pandemic mainly for the purpose of fact-finding. Regarding the effects of rumor sharing behavior on life satisfaction, although sharing wish rumors (i.e., rumors expressing hopes) had no effect on sharers' life satisfaction, sharing dread rumors (i.e., rumors reflecting fears) and aggression rumors (i.e., rumors implying aggression and hatred) reduced sharers' life satisfaction. This research lends support to the integrative model of rumor and provides practical implications for mitigating the spread of rumors.

FRD: Few-shot rumor detection for the novel coronavirus pneumonia epidemic

With the full arrival of the digital era, fueled by both information technology and business marketing, rumors are produced and spread endlessly on social networks. During the recent novel coronavirus pneumonia epidemic, online rumors have continued to flourish. Most existing studies on traditional rumor detection rely on a large number of features in practical applications. However, the current severe epidemic scenarios have limited rumor information features, and it remains a challenging problem to detect epidemic rumors with high accuracy using only limited information. As a result, we propose a novel Few-shot Rumor Detection model (FRD) for the novel coronavirus pneumonia, which is combined with meta-learning to be able to accurately identify rumors as soon as possible in crises. Specifically, we started by using the BERT+BiLSTM combination for rumor text feature extraction and representation to generate the historical rumor sample-wise vector and epidemic rumor sample-wise vector;secondly, the prototypical network was introduced to summarize the historical rumor data, and the feature vectors of samples belonging to the same category were averaged to obtain the prototype representation of historical rumor category;finally, we utilize the modified cosine similarity measure function to calculate the distance between the class-wise vector of historical rumor text and the sample-wise vector of epidemic rumor, and complete the rumor detection according to the nearest neighbor method. Our experimental results on English datasets show that the FRD rumor detection model proposed in this paper is superior to other baseline algorithms in terms of accuracy, precision, recall and macro F1 value. From the comparison of experimental results, the FRD model can effectively improve conventional rumor detection methods, and better realize the early detection of sudden epidemic rumors. © 2022 IEEE.

Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal;nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model © 2013 IEEE.

Disinformation in Social Networks: A Systematic Review on Fake News in Times of Pandemic

The use of social media, low literacy, fast information sharing and preprint services are identified as the main causes of the infodemic [4] and among its consequences we find that it can promote public health risk behaviors globally. The results of Fake news represents a threat to societies in the context of the pandemic. The aim of this article is to review existing research on fake news in the last 2 years, discussing the characteristics of infodemics, media/digital literacy and its impact on society, as well as highlighting mechanisms to detect and curb fake news on covid-19 in social networks. Thirty articles were analyzed and selected from 1354 open access articles on this subject. The conclusion was that knowledge of fake news should be taken note of due to the harmful effects on society, considering the informational contexts (epistemic, normative and emotional), together with media literacy to increase trust and emphasize public health messages with emotionally relevant and scientifically based content, in order to continue conducting research that allows a 100% effective recognition and elimination of untruthful information on social networks. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Strategies and effectiveness of the chinese government debunking covid-19 rumors on sina weibo: Evaluating from emotions

The Chinese government refuted rumors on social media for infodemic management when COVID-19 outbroke. This study selected 80 government accounts on Sina Weibo and collected 501 valid anti-rumor posts with comments from 18 January to 29 February 2020. This paper evaluated the effectiveness of rumor debunking from the public emotions reflected in the comments. This study also examined the influence of different anti-rumor strategies, such as fact-checking, rumor response modes, and presentation forms, on the effectiveness of rumor debunking. The findings revealed that fact-checking, combined response mode and text presentation could improve the effectiveness of rumor debunking to some extent. Further analysis of the public emotions indicated a correlation between the trust in government and the effectiveness of rumor debunking. These findings suggested building a multiparticipant response mechanism with medical institutions and media to mitigate the COVID-19 infodemic through targeted strategies, thus further increasing the government's credibility via information governance. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Book Review: Disinformation in the Global South by Herman Wasserman & Dani Madrid-Morales (Eds.)

Facing a culture of disinformation and misinformation in Kinshasa, media users follow strategies to produce alternative news media or navigate various media platforms, comparing and checking information with other sources. As much as this might upset some scholars willing to build a rigid field, a research agenda embracing the Global South and different contexts brings much more valuable contributions. Mainstream media in the Global South is perceived as a vector of disinformation, in a context of low pluralism and high political parallelism. [Extracted from the article] Copyright of International Journal of Press/Politics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The impact of epidemic information on the public's worries and attitude toward epidemic prevention measures during the COVID-19 outbreak.

Since the outbreak of 2019 coronavirus disease (COVID-19) in December 2019, the Chinese government has implemented effective epidemic prevention measures. To provide useful information for governments to manage this public health crisis, we conducted an online survey among Chinese general population from February 24 to 28, 2020. In this study, we examined the impact of epidemic information and rumors on public's worries and attitude toward prevention measures during the outbreak of COVID-19. A total of 853 valid questionnaires (641 women, 75.1%) were collected from 24 provincial regions in China. Most respondents' ages ranged from 18 to 60 (833 participants, 97.66%). A mediation model was built to analyze the influence of epidemic information and rumors on worries and attitude. The results showed that the amount of epidemic information positively predicted public's worries, which in turn predicted a supportive attitude toward the prevention measures. Worries partially mediated the relationship between the amount of epidemic information and the supportive attitude. The amount of rumors negatively predicted the supportive attitude. The results of this study implied the importance of timely and credible information providing to evoke a certain level of worry and promote public cooperation, and the necessary attention to refute and resist rumors for effective risk communication in a public health crisis.

Evaluating the generalisability of neural rumour verification models

Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem. © 2022 The Author(s)

How rumors diffuse in the infodemic: Evidence from the healthy online social change in China

Infodemic is defined as 'an overabundance of information-some accurate and some not-that makes it hard for people to find trustworthy sources and reliable guidance when they need it' by the World Health Organization. As unverified information, rumors can widely spread in online society, further diffusing infodemic. Existed studies mainly focused on rumor detection and prediction from the statement itself and give the probability that it will evolve into a rumor in the future. However, the detection and prediction from rumors production perspective is lack. This research explores the production mechanism from the uncertainty perspective using the data from Weibo and public rumor data set. Specifically, we identify the public uncertainty through usergenerated content on social media based on systemic functional linguistics theory. Then we empirically verify the promoting effect of uncertainty on rumor production and constructed a model for rumor prediction. The fitting effect of the empirical model with the public uncertainty is significantly better than that with only control variables, indicating that our framework identifies public uncertainty well and uncertainty has a significantly predictive effect on rumors. Our study contributes to the research of rumor prediction and uncertainty identification, providing implications for healthy online social change in the post-epidemic era.

People with High Perceived Infectability Are More Likely to Spread Rumors in the Context of COVID-19: A Behavioral Immune System Perspective.

Since the outbreak of COVID-19, many studies have explored the influencing factors of rumor spreading, such as anxiety, risk perception and information source credibility, but few studies have focused on the impact of individual differences. Based on the theory of behavioral immune systems, we investigated the impact of perceived infectability on rumor spreading and the mediating role of rumor trust in the context of COVID-19. Two studies were investigated using the scale and recall-report task of rumor spreading. The results show that perceived infectability was a significant positive predictor of rumor spreading. However, the impact of perceived infectability on rumor spreading was not direct, and it mainly indirectly affected rumor spreading through the mediating role of rumor trust. Overall, the findings suggest that individuals with high perceived infectability are more likely to believe rumors and then spread rumors during the epidemic. This study advances the literature on rumor spreading and behavioral immune systems and provides practical implications to anti-rumor campaigns.

A survey on the public's discrimination rate of typical rumors during the coronavirus disease 2019 epidemic. / æ–°åž‹å† çŠ¶ç— æ¯’è‚ºç‚Žæµè¡ŒæœŸé—´å ¬ä¼—å¯¹å ¸åž‹è°£è¨€æ­£ç¡®ç”„åˆ«çŽ‡çš„è°ƒæŸ¥.

OBJECTIVES: During the epidemic of coronavirus disease-2019 (COVID-19), the wide spread of rumors caused significant public hazards. This study aims to understand the situation of discrimination for typical COVID-19 rumors by the public and related factors. METHODS: An anonymous online survey was carried out using Questionnaire Star. The contents included participants' gender, age, education level, the COVID-19 information sources, and the judgmental questions about 14 representative COVID-19 rumors. The discrimination rate and 95% confidence interval of 14 rumors were estimated, and the association of discrimination rate with gender, age, and education level was analyzed by binary logistic regression. RESULTS: A total of 2 087 valid questionnaires were collected. The participants were mainly female (62.7%) and below 35 years old (63.4%); the education level was predominantly college/bachelor's degree (47.3%) and master's degree or above (39.1%); the participants, who accessed to COVID-19 information included internet media, accounted for 91%. The participants with different gender, age, and education level had significant differences in the distribution of COVID-19 information sources (all P<0.01). The participants' discrimination rate for 14 rumors ranged from 67.4% to 98.6%, with 4 rumors less than 80%. Women's discrimination rate of 9 rumors was significantly higher than men's (all P<0.05). There was no significant difference in the discrimination rate of rumors among the different age groups (all P>0.05), but the differences in the discrimination rate of other rumors among the different age groups varied according to the rumor. Compared to those with high school or less education levels, the discrimination rates were also higher in the respondents with high education levels (P<0.05). CONCLUSIONS: A few publics are still unable to identify typical rumors during the COVID-19 epidemic. There are associations among genders, age, and the education levels with the discrimination of some rumors. The government authorities should strengthen the true information regarding COVID-19, and therefore enhance the public's ability to identify rumors.

Configurational patterns for COVID-19 related social media rumor refutation effectiveness enhancement based on machine learning and fsQCA.

Infodemics are intertwined with the COVID-19 pandemic, affecting people's perception and social order. To curb the spread of COVID-19 related false rumors, fuzzy-set qualitative comparative analysis (fsQCA) is used to find configurational pathways to enhance rumor refutation effectiveness. In this paper, a total of 1,903 COVID-19 related false rumor refutation microblogs on Sina Weibo are collected by a web crawler from January 1, 2022 to April 20, 2022, and 10 main conditions affecting rumor refutation effectiveness index (REI) are identified based on "three rules of epidemics". To reduce data redundancy, five ensemble machine learning models are established and tuned, among which Light Gradient Boosting Machine (LGBM) regression model has the best performance. Then five core conditions are extracted by feature importance ranking of LGBM. Based on fsQCA with the five core conditions, REI enhancement can be achieved through three different pathway elements configurations solutions: "Highly influential microblogger * high followers' stickiness microblogger", "high followers' stickiness microblogger * highly active microblogger * concise information description" and "high followers' stickiness microblogger * the sentiment tendency of the topic * concise information description". Finally, decision-making suggestions for false rumor refutation platforms and new ideas for improving false rumor refutation effectiveness are proposed. The innovation of this paper reflects in exploring the REI enhancement strategy from the perspective of configuration for the first time.

Relationship between Health Literacy and Acceptance of Rumors Related to COVID-19 Vaccination among Iranian Adult Population

Background: Rumors concerning various aspects of the fight against COVID-19, vaccination, in particular, have become one of the main challenges for managers and policymakers who have to deal with different aspects of the disease. This necessitates the recognition of the factors that influence the prevention and spread of these rumors. Objective(s): The current study aimed to investigate the link between health literacy among adults and their acceptance of COVID-19 vaccination rumors in Iran. Method(s): This cross-sectional study was conducted from November 15 to December 15, 2021, in different provinces of Iran. The study population included Iranian adults, aged 18 years and older, who were selected using the snowball sampling method. The data collection tools involved two questionnaires: the Health Literacy Questionnaire, which consists of 33 items, and the COVID-19 Vaccine Rumor Questionnaire which assesses 17 rumors related to COVID-19 vaccination collected from various news sources. Result(s): The number of completed questionnaires was 1158 out of 2163 questionnaire visits (74% response rate). Univariate analysis showed that health literacy had a statistically significant association with sociodemographic variables of gender, marital status, ethnicity, place of residence, and level of education. The results of data analysis also demonstrated a significant correlation between the average of rumors' acceptance and the sociodemographic variables of gender, marital status, ethnicity, place of residence, and level of education. The results of the Pearson correlation coefficient test showed a significant and negative relationship between health literacy and rumor belief (P= 0.000, r=-0.590), indicating that those with a higher level of health literacy had a lower level of rumor acceptance. Conclusion(s): Based on the findings of the present study, health literacy has a significant effect on reducing the credibility of rumors and other misinformation among community members. Macro-level decisions and policies are needed to improve factors such as health literacy and can help individuals identify and track rumors and make decisions based on reliable information on vaccination. Copyright © 2022, Author(s).

What do 5G networks, Bill Gates, Agenda 21, and QAnon have in common? Sources, distribution, and characteristics

Mounting uncertainties regarding the coronavirus disease (COVID-19) pandemic and the popularity of social media created fertile grounds for conspiracy theories to flourish, leading to a global "infodemic." We examine information sources used to support five popular COVID-19-related conspiracy theories on Twitter to identify (1) their primary building blocks, (2) similarities and dissimilarities across COVID-19 conspiracy theories, and (3) the relationship between type of message content and content distribution. Findings show that statements of belief and of malicious purpose were most popular, followed by conspirators, authentication, and secretive actions. However, only malicious purposes and secretive actions messages successfully predicted higher distribution of content, while, for instance, content authentication showed a negative relation. Furthermore, the type of conspiracy theories matters. Mega-theories, such as Agenda 21 and QAnon, incorporated less statements of Belief. COVID-19 vaccine-related theories focused more on authentication, while QAnon highlighted the conspirators behind the pandemic. Conceptual and practical implications are discussed.

Rumors of Social Networks and their Role in Influencing the Degree of Citizens Attitude Regarding Vaccination with the Covid-19 Vaccine: A Field Study

The present study sought to investigate social network rumors and their function in affecting a citizen's desire to get vaccinated with the Corona Vaccine, as well as to evaluate the social networks that are most commonly utilized in disseminating rumors regarding the Corona Vaccine. Furthermore, this study looked at the vaccinations that were most influenced by the misinformation. The current study involved 400 individuals via internet questionnaires. The survey revealed that the majority of respondents (95.2 %) believe that rumors circulate on social networks and that the degree of diffusion of these rumors is considerable (66.8 %). While other social networks were significantly affected, Facebook had the most at (68.9 %). According to the study, periods when rumors propagate on social media are when diseases and epidemics spread. It was discovered that the purpose of disseminating rumors about the vaccination on social media was to instill fear in residents and lead to a misunderstanding of the genuine scientific truth. According to the study, the most rumored vaccination via social networks was AstraZeneca at (65.1 %). The most widely circulated rumor regarding the vaccine on social networks was that it "causes sterility”. [ FROM AUTHOR]

Multi-dimensional characteristics and governance strategies of rumors about the COVID-19 pandemic

This study was based on the rumor data of the National Joint Anti- Rumor Online Platform, data mining and textual analysis were used to analyze the contents of rumors from the spatial, temporal, and semantic dimensions. The results indicated that the number of rumors showed a relatively consistent trend with the development of the pandemic, spatial distributions of rumors were relatively uneven, exhibiting a concentration in large cities. In addition, rumor keywords in different spatiotemporal contexts were all related to the themes of the prevailing outbreak situation and the prevention and control measures, however, the obvious differences were seen as well. The number of rumors was positively correlated with the number of confirmed cases and was easily affected by external factors. © 2022 IEEE.

Rumor Localization, Detection and Prediction in Social Network

With the global epidemic of the COVID-19, various rumors spread wantonly on social networks, which has seriously affected the stability and harmony of the entire society. To purify the network environment, some researchers have proposed to fight rumors from the perspectives of tracing the source of rumors, detecting the authenticity of information, and predicting explosive fake news. But their works are fragmented, and their performance are not significant. So we need strong antirumor methods to fight rumors. To this end, this article proposes a more comprehensive antirumor mechanism, which can realize rumors source location, rumor detection, and popularity prediction (RLDP). In particular, in the task of localization, we propose graph neural network-based method, which does not need to specify the underlying propagation mode and the number of rumor sources;in the task of detection, utilizing lightGBM, we construct a rumor detection model;in the task of popularity prediction, we construct a model based on contrastive learning while considering user engagements and information propagation, and the text of rumor. Finally, we verify the performance of the proposed RLDP by conducting extensive experiments. IEEE