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.

Rumor Detection on Social Media Using Deep Learning Algorithms with Fuzzy Inference System for Healthcare Analytics System Using Covid-19 Dataset

Spreading rumors on social media is a phenomenon that has destructive implication of societal interaction, diverts attention toward destructive behavior. The impact will be more influenced in healthcare management. This research aims to detect the rumors and identify the sources using deep learning algorithms. In our proposed system, after pre-processing, the tweet comments are extracted from topics and ranked as deny, support, query and comment. Then the comments are classified as positive, negative and neutral using Artificial Neural Network Neuro-fuzzy Inference System Spline-based pi-shaped Membership Function (ANISPIMF). Then the negative comments are classified into offensive, violence, misogyny and hate mongering by using Improved Deep Learning Neural Network (IDLNN) which is the combination of Deep Neural Network with Cuckoo Search–Flower Pollination Algorithm to optimize the weight values. The optimized ANISPIMF performs very well for the COVID-19 dataset in terms of Accuracy, Precision and Recall. The proposed system attains better performance and efficiency when weighted against prevailing methodologies — regarding the performance measures, there is an improvement of accuracy by 0.6%, recall by 0.7%, and precision by 1%, together with an F1-score of 1.2% than the Multiloss Hierarchical Bi-LSTM with Attenuation Factor (MHA). [ABSTRACT FROM AUTHOR] Copyright of International Journal of Computational Intelligence & Applications is the property of World Scientific Publishing Company 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

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).

Antecedents and Consequences of Misinformation Sharing Behavior among Adults on Social Media during COVID-19.

Misinformation has been existed for centuries, though emerge as a severe concern in the age of social media, and particularly during COVID-19 global pandemic. As the pandemic approached, a massive influx of mixed quality data appeared on social media, which had adverse effects on society. This study highlights the possible factors contributing to the sharing and spreading misinformation through social media during the crisis. Preferred Reporting Items and Meta-Analysis guidelines were used for systematic review. Anxiety or risk perception associated with COVID-19 was one of the significant motivators for misinformation sharing, followed by entertainment, information seeking, sociability, social tie strength, self-promotion, trust in science, self-efficacy, and altruism. WhatsApp and Facebook were the most used platforms for spreading rumors and misinformation. The results indicated five significant factors associated with COVID-19 misinformation sharing on social media, including socio-demographic characteristics, financial considerations, political affiliation or interest, conspiracy ideation, and religious factors. Misinformation sharing could have profound consequences for individual and society and impeding the efforts of government and health institutions to manage the crisis. This SLR focuses solely on quantitative studies, hence, studies are overlooked from a qualitative standpoint. Furthermore, this study only looked at the predictors of misinformation sharing behavior during COVID-19. It did not look into the factors that could curb the sharing of misinformation on social media platforms as a whole. The study's findings will help the public, in general, to be cautious about sharing misinformation, and the health care workers, and institutions, in particular, for devising strategies and measures to reduce the flow of misinformation by releasing credible information through concerned official social media accounts. The findings will be valuable for health professionals and government agencies to devise strategies for handling misinformation during public health emergencies.

COVID-19 and Rumors: A Dynamic Nested Optimal Control Model.

Unfortunately, the COVID-19 outbreak has been accompanied by the spread of rumors and depressing news. Herein, we develop a dynamic nested optimal control model of COVID-19 and its rumor outbreaks. The model aims to curb the epidemics by reducing the number of individuals infected with COVID-19 and reducing the number of rumor-spreaders while minimizing the cost associated with the control interventions. We use the modified approximation Karush-Kuhn-Tucker conditions with the Hamiltonian function to simplify the model before solving it using a genetic algorithm. The present model highlights three prevention measures that affect COVID-19 and its rumor outbreaks. One represents the interventions to curb the COVID-19 pandemic. The other two represent interventions to increase awareness, disseminate the correct information, and impose penalties on the spreaders of false rumors. The results emphasize the importance of interventions in curbing the spread of the COVID-19 pandemic and its associated rumor problems alike.

Starts and refutations of the Covid-19 rumors: Evidence from the reaction of the stock market

By manually collecting data on Internet-based rumors concerning COVID-19, we investigate the market reactions to the spread of such rumors and the gov-ernment's refutation of them. We find that frightening (reassuring) rumors have a negative (positive) impact on investors. The refutation of frightening rumors triggers a positive market response, whereas the refutation of reassur-ing rumors does not cause a significant market reaction. Further analysis shows that there is a stock price drift when frightening rumors are refuted by governments. Our conclusions remain robust after considering endogeneity. Our findings support the notion that epidemic-related rumors affect investors' decisions, which add to literatures of the market responses of companies in the context of the COVID-19 pandemic and provide incremental evidence for the "the spiral of silence" theory.(c) 2022 Sun Yat-sen University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecom-mons.org/licenses/by-nc-nd/4.0/).

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.

The Effects of Personality Traits on Rumors

The spread of rumors has often been linked to major social and political impacts with consequences that oftentimes may prove to be severe. While there are multiple factors that could make a rumor more believable, this paper focuses on investigating the effects of personality traits on believing or disbelieving rumors. Participants were given a survey which included rumors relating to a single topic, COVID-19, to avoid topic-bias. Participants were also given a personality test which assessed the participants’ traits based on the Big 5 Model and categorized them as high or low. The effect of valence (pleasure) and arousal (excitement) on believing or disbelieving rumors was also explored, along with how this effect differs from one trait to another. The results showed that people with high agreeableness tend to believe rumors more than people with low agreeableness and that there was a correlation between valence and believing rumors for people with high neuroticism and people with low agreeableness. No correlation was found between arousal and believing rumors for any of the personality traits. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Starts and Refutations of the Covid-19 Epidemic Rumors: Evidence from the Reaction of the Stock Market

By manually collecting data on Internet-based rumors concerning COVID-19, we investigate the market reactions to the spread of such rumors and the government’s refutation of them. We find that frightening (reassuring) rumors have a negative (positive) impact on investors. The refutation of frightening rumors triggers a positive market response, whereas the refutation of reassuring rumors does not cause a significant market reaction. Further analysis shows that there is a stock price drift when frightening rumors are refuted by governments. Our conclusions remain robust after considering endogeneity. Our findings support the notion that epidemic-related rumors affect investors’ decisions, which add to literatures of the market responses of companies in the context of the COVID-19 pandemic and provide incremental evidence for the “the spiral of silence” theory.

A Cross-sectional Study of Antecedents and Consequence of Panic Buying Behavior: The Moderating Effect of COVID-19 Rumors.

The novel corona virus pandemic has influenced people buying behaviors. Due to the significant psychological and behavioral impact of COVID-19 on society, this study aimed to examine the determinants of panic buying behavior and a resultant psychological outcome in the form of a sense of security. The purpose of this study is to investigate the effect of COVID-19 caller ringback tone (CRT) experiences, that is, informational and stimulation experience, on the panic buying behavior and how rumors moderate this relationship. This research is quantitative and uses a purposive sampling method to collect the survey-based data from 264 respondents. The researchers analyzed the data using Partial Least Square Structural Equation Modeling (PLS-SEM). The results of data analysis indicated that the informational and stimulation experience of COVID-19 CRT had a significant influence on panic buying behavior which further resulted in a sense of security in public. This study could not find evidence of the moderating role of rumors in the relationship between COVID-19 CRT experiences and panic buying behavior. The findings highlight the role of the COVID-19 CRT in causing panic buying behavior and resultant psychological outcome and thus provide implications for policymakers on the control of panic buying under COVID-19.

Content characteristics predict the putative authenticity of COVID-19 rumors.

Rumors regarding COVID-19 have been prevalent on the Internet and affect the control of the COVID-19 pandemic. Using 1,296 COVID-19 rumors collected from an online platform (piyao.org.cn) in China, we found measurable differences in the content characteristics between true and false rumors. We revealed that the length of a rumor's headline is negatively related to the probability of a rumor being true [odds ratio (OR) = 0.37, 95% CI (0.30, 0.44)]. In contrast, the length of a rumor's statement is positively related to this probability [OR = 1.11, 95% CI (1.09, 1.13)]. In addition, we found that a rumor is more likely to be true if it contains concrete places [OR = 20.83, 95% CI (9.60, 48.98)] and it specifies the date or time of events [OR = 22.31, 95% CI (9.63, 57.92)]. The rumor is also likely to be true when it does not evoke positive or negative emotions [OR = 0.15, 95% CI (0.08, 0.29)] and does not include a call for action [OR = 0.06, 95% CI (0.02, 0.12)]. By contrast, the presence of source cues [OR = 0.64, 95% CI (0.31, 1.28)] and visuals [OR = 1.41, 95% CI (0.53, 3.73)] is related to this probability with limited significance. Our findings provide some clues for identifying COVID-19 rumors using their content characteristics.

#RumorsCOVID-19: Predicting the Forwarding of Online Rumors in Wuhan, China and in Israel: International Journal for Mass Communication studies

COVID-19 ushered in almost unprecedented socioeconomic and political challenges. A typical social reaction during such emergencies is rumormongering, which has intensified since the advent of social media. This study explored factors affecting users’ willingness to spread pandemic-related rumors in Wuhan, China and Israel. We tested a multi-variant model of factors affecting the forwarding of COVID-19 rumors. In an online survey conducted in April–May 2020, users of each country's leading social media platform (WeChat and WhatsApp, respectively) reported on patterns of exposure to and spread of COVID-19 rumors, as well as on their motives for doing so. Despite major differences between the two societies, interesting similarities were found: in both cases, individual drives, shaped by personal needs and degree of negative feelings, were the leading factors behind rumormongering. Exposure to additional sources of information regarding the rumors was also a significant predictor, but only in the Chinese case.

Identification and Analysis of Opinion Leaders for COVID-19 Related Rumor Spreading in Social Media

Since the outbreak of COVID-19, thousands of rumors have occurred on social media, and it is significant to identify opinion leaders who play decisive roles during rumor spreading. However, existing literature lacks such opinion leaders identification and following analysis of COVID-19 background. So this paper takes a COVID-19 case as an example and collects data from Sina Weibo, which is a popular twitter-like social media in China. Then three different centrality measures are applied. Finally, a venn diagram is used to analyze opinion leaders identified, and profiles of them on Weibo are taken into consideration. In conclusion, the paper finds that opinion leaders identified during rumor spreading are institutional and individual accounts with a huge number of followers. But in terms of numbers, government institutions spread information to more people;in terms of breadth, impactful individual accounts deliver more information to more people from all walks of life. © VDE VERLAG GMBH - Berlin - Offenbach.

Health-Related Rumor Control through Social Collaboration Models: Lessons from Cases in China during the COVID-19 Pandemic.

Widely spread health-related rumors may mislead the public, escalate social panic, compromise government credibility, and threaten public health. Social collaboration models that maximize the functions and advantages of various agents of socialization can be a promising way to control health-related rumors. Existing research on health-related rumors, however, is limited in studying how various agents collaborate with each other to debunk rumors. This study utilizes content analysis to code the text data of health-related rumor cases in China during the COVID-19 pandemic. The study found that socialized rumor-debunking models could be divided into the following five categories: the government-led model, the media-led model, the scientific community-led model, the rumor-debunking platform-led model, and the multi-agent collaborative model. In addition, since rumors in public health crises often involve different objects, rumor refutation requires various information sources; therefore, different rumor-debunking models apply. This study verifies the value of socialized collaborative rumor debunking, advocates and encourages the participation of multiple agents of socialization and provides guidance for establishing a collaborative rumor-debunking model, thereby promoting efficient rumor-debunking methods and improving the healthcare of society.

Trust in government buffers the negative effect of rumor exposure on people's emotions.

Although we are surrounded by various kinds of rumors during the coronavirus disease pandemic, little is known about their primary content, what effect they might have on our emotions, and the potential factors that may buffer their effect. Combining qualitative (study 1 extracted 1907 rumors from top rumor-refuting websites using the Python Web Crawler and conducted content analysis) and quantitative (study 2 conducted an online survey adopting a three-wave design, N = 444) research methods, the current study revealed that government-related rumors accounted for the largest proportion of rumors during the outbreak stage of the pandemic and were positively associated with the public's negative emotions. We also found that trust in government negatively moderated the relationship between government-related rumors and negative emotions. Specifically, when people had low trust in government, exposure to government-related rumors was positively associated with negative emotions. However, when people had high trust in government, the association was non-significant. For positive emotions, we found no significant effects of government-related rumors. The findings highlight the importance of rumor control during public emergencies and cultivating public trust in government in the long run. Supplementary Information: The online version contains supplementary material available at 10.1007/s12144-022-03508-x.

Prevention of Covid-19 Health Rumors Based on Elder Users Oriented Serious Games

This paper concerns health rumors that emerged during the outbreak of novel coronavirus. A serious game was designed as an experimental program for the prevention and control of health rumors. This covers two parts that include the TCP model for the games and cognitive questionnaires. The relevant variables of the experimental study are defined and the hypothesis is proposed. Two hundred experimental subjects are selected to participate in the experiments. Through the collection of relevant data in the experiments, statistical observations and comparative analysis are conducted to test whether the experimental hypothesis can be established. In addition, influence factors for the judgment and recognition of health rumors are considered in the comparison and analysis. © 2021 IEEE.

A Comparative Study of Machine Learning Approaches for Rumors Detection in Covid-19 Tweets

Analysing social media content becomes a crucial task due to the tremendous usage of social media platforms. In the era of COVID-19, detecting rumors becomes a vital task. In natural language processing, detecting rumors is a challenging task due to the complexity of rumors and tracking the source of rumors. In this paper, we proposed a machine learning-based model for rumors detection in COVID-19 related tweets for both English and Arabic Languages. Different machine learning algorithms have been implemented and Term Frequency/Inverse Document Frequency tf/idf has been used for feature extraction. The performance of all implemented classifiers has been analysed and compared. Our approach does not use external resources or data and depends only on the given training data. © 2022 IEEE.

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.

Deep Negative Effects of Misleading Information about COVID-19 on Populations Through Twitter

If pandemics kill humans and spread too quickly, misinformation is another scourge that puts people in danger. Health is what a person needs the most in the world to strive for great wealth and a bright future. The novel Coronavirus Disease (COVID-19) outbreak has threatened massively human health in the 21 century (precisely in 2020). The spreading of COVID-19 pandemic press specialists to do more efforts to find a cure. The same reason makes people perform billions of queries on search engines and social networks about comprehending the origin of the virus, the spread mechanisms and existent cures. The virus that causes the pandemic is the new Coronavirus appeared in a unique market in Wuhan, in China in December 2019. This new Coronavirus is named coronavirus (COVID-19). Throughout the ages, mankind has experienced many epidemics, but the distinction of the 21 century is technology development. The spread of misinformation is faster than that of the pandemic. With the advent of big data, we can analyze the huge information shared in a second in social networks and it contains millions of misinformation. In this current, we analyze the belief frequency of misinformation in three languages, English, French and Arabic shared on Twitter users’ timelines. Misinformation urges people against vaccination in different ways;many people are spreading misinformation to be famous or make money through views and sharing. Scientists and Journalists are concerned to reduce the likelihood of susceptibility to misinformation by complying with WHO guidance measures in social networks. © 2022 International Information and Engineering Technology Association. All rights reserved.