Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data.

COVID-19 is a respiratory infectious disease that first reported in Wuhan, China, in December 2019. With COVID-19 spreading to patients worldwide, the WHO declared it a pandemic on 11 March 2020. This study collected 1,746,347 tweets from the Korean-language version of Twitter between February and May 2020 to explore future signals of COVID-19 and present response strategies for information diffusion. To explore future signals, we analyzed the term frequency and document frequency of key factors occurring in the tweets, analyzing the degree of visibility and degree of diffusion. Depression, digestive symptoms, inspection, diagnosis kits, and stay home obesity had high frequencies. The increase in the degree of visibility was higher than the median value, indicating that the signal became stronger with time. The degree of visibility of the mean word frequency was high for disinfectant, healthcare, and mask. However, the increase in the degree of visibility was lower than the median value, indicating that the signal grew weaker with time. Infodemic had a higher degree of diffusion mean word frequency. However, the mean degree of diffusion increase rate was lower than the median value, indicating that the signal grew weaker over time. As the general flow of signal progression is latent signal → weak signal → strong signal → strong signal with lower increase rate, it is necessary to obtain active response strategies for stay home, inspection, obesity, digestive symptoms, online shopping, and asymptomatic.

Developing an agent-based model to minimize spreading of malicious information in dynamic social networks.

This research introduces a systematic and multidisciplinary agent-based model to interpret and simplify the dynamic actions of the users and communities in an evolutionary online (offline) social network. The organizational cybernetics approach is used to control/monitor the malicious information spread between communities. The stochastic one-median problem minimizes the agent response time and eliminates the information spread across the online (offline) environment. The performance of these methods was measured against a Twitter network related to an armed protest demonstration against the COVID-19 lockdown in Michigan state in May 2020. The proposed model demonstrated the dynamicity of the network, enhanced the agent level performance, minimized the malicious information spread, and measured the response to the second stochastic information spread in the network.

The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users During the COVID-19 Pandemic

The outbreak of the COVID-19 pandemic triggers infodemic over online social media, which significantly impacts public health around the world, both physically and psychologically. In this paper, we study the impact of the pandemic on the mental health of influential social media users, whose sharing behaviours significantly promote the diffusion of COVID-19 related information. Specifically, we focus on subjective well-being (SWB), and analyse whether SWB changes have a relationship with their bridging performance in information diffusion, which measures the speed and wideness gain of information transmission due to their sharing. We accurately capture users' bridging performance by proposing a new measurement. Benefiting from deep-learning natural language processing models, we quantify social media users' SWB from their textual posts. With the data collected from Twitter for almost two years, we reveal the greater mental suffering of influential users during the COVID-19 pandemic. Through comprehensive hierarchical multiple regression analysis, we are the first to discover the strong relationship between social users' SWB and their bridging performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Exploring the Impact of the Quality of Social Media Early Adopters on Vaccine Adoption

As social media such as Twitter has become an important medium for disseminating information, it is essential to understand how the information diffusion on social media influences public adoption of vaccines. Based on the innovation diffusion theory, we construct a user and information quality indicator system for early adopters of COVID-19 vaccination by identifying their creation of user-generated content on social media. Machine learning approaches and text analysis methods are used to perform topic clustering and sentiment analysis on vaccination-related tweets on Twitter. Based on each country's vaccination data in January 2021, the study examines the relationship between the quality of social media early adopters, and the quality of the information they publish with vaccine adoption by using the OSL regression model. The empirical results show that the total number of tests, the number of new COVID-19 cases, and the human development index have a significantly positive influence on vaccine adoption. Neutral emotions and offensive language of early adopters on social media have a significantly negative relationship with vaccine adoption. These interesting findings can help governments and public health officials understand early adopters' perceptions of vaccines and play an important role in targeted policy interventions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks.

In recent years, as the COVID-19 global pandemic evolves, many unprecedented new patterns of epidemic transmission continue to emerge. Reducing the impact of negative information diffusion, calling for individuals to adopt immunization behaviors, and decreasing the infection risk are of great importance to maintain public health and safety. In this paper, we construct a coupled negative information-behavior-epidemic dynamics model by considering the influence of the individual's self-recognition ability and physical quality in multiplex networks. We introduce the Heaviside step function to explore the effect of decision-adoption process on the transmission for each layer, and assume the heterogeneity of the self-recognition ability and physical quality obey the Gaussian distribution. Then, we use the microscopic Markov chain approach (MMCA) to describe the dynamic process and derive the epidemic threshold. Our findings suggest that increasing the clarification strength of mass media and enhancing individuals' self-recognition ability can facilitate the control of the epidemic. And, increasing physical quality can delay the epidemic outbreak and leads to suppress the scale of epidemic transmission. Moreover, the heterogeneity of the individuals in the information diffusion layer leads to a two-stage phase transition, while it leads to a continuous phase transition in the epidemic layer. Our results can provide favorable references for managers in controlling negative information, urging immunization behaviors and suppressing epidemics.

Network distribution and sentiment interaction: Information diffusion mechanisms between social bots and human users on social media

When public health emergencies occur, a large amount of low-credibility information is widely disseminated by social bots, and public sentiment is easily manipulated by social bots, which may pose a potential threat to the public opinion ecology of social media. Therefore, exploring how social bots affect the mechanism of information diffusion in social networks is a key strategy for network governance. This study combines machine learning methods and causal regression methods to explore how social bots influence information diffusion in social networks with theoretical support. Specifically, combining stakeholder perspective and emotional contagion theory, we proposed several questions and hypotheses to investigate the influence of social bots. Then, the study obtained 144,314 pieces of public opinion data related to COVID-19 in J city from March 1, 2022, to April 18, 2022, on Weibo, and selected 185,782 pieces of data related to the outbreak of COVID-19 in X city from December 9, 2021, to January 10, 2022, as supplement and verification. A comparative analysis of different data sets revealed the following findings. Firstly, through the STM topic model, it is found that some topics posted by social bots are significantly different from those posted by humans, and social bots play an important role in certain topics. Secondly, based on regression analysis, the study found that social bots tend to transmit information with negative sentiments more than positive sentiments. Thirdly, the study verifies the specific distribution of social bots in sentimental transmission through network analysis and finds that social bots are weaker than human users in the ability to spread negative sentiments. Finally, the Granger causality test is used to confirm that the sentiments of humans and bots can predict each other in time series. The results provide practical suggestions for emergency management under sudden public opinion and provide a useful reference for the identification and analysis of social bots, which is conducive to the maintenance of network security and the stability of social order. © 2022

Validation of an Information Diffusion based Health Social Media on Yoga and Ayurveda

The deadly COVID-19 coupled with other diseases has proven to be the biggest challenge humans have seen in ages. Healthcare systems, even in the most developed countries, have completely shattered during the peak of waves. Thus, leaving millions rendered helpless and alone around the globe. This proves the importance of self-care and immunity to being the best possible way for being healthy. Solutions specific to strengthening immunity are available in the Indian sciences of Yoga and Ayurveda and has been scientifically proven. These have been around in India for ages but there is an immense lack of awareness in India and around the globe regarding that. This review paper aims at filling the lack of awareness by proposing a model and validating it further by collecting and comparing the pre and post-implementation data. © 2022 IEEE.

#CoronaVirus and public health: the role of social media in sharing health information

Purpose> The purpose of this study is to examine how factual information and misinformation are being shared on Twitter by identifying types of social media users who initiate the information diffusion process.Design/methodology/approach> This study used a mixed methodology approach to analyze tweets with COVID-19-related hashtags. First, a social network analysis was conducted to identify social media users who initiate the information diffusion process, followed by a quantitative content analysis of tweets by users with more than 5K retweets to identify what COVID-19 claims, factual information, misinformation and disinformation was shared on Twitter.Findings> Results found very little misinformation and disinformation distributed widely. While health experts and journalists shared factual COVID-19-related information, they were not receiving optimum engagement. Tweets by citizens focusing on personal experience or opinions received more retweets and likes compared to any other sender type. Similarly, celebrities received more replies than any other sender type.Practical implications> This study helps medical experts and government agencies understand the type of COVID-19 content and communication being shared on social media for population health purposes.Originality/value> This study offers insight into how social media users engage with COVID-19-related information on Twitter and offers a typology of categories of information shared about the pandemic.Peer review> The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-03-2021-0143/.

Anatomy of # Plandemi Infodemic: A Case Study on Misinformation Diffusion During the Covid-19 Pandemic

Just as the COVID-19 pandemic has spread with the transportation networks of the globalizing world, the infodemic is spreading globally with social media networks. The diffusion potential of social media with its complex structure of mass and interpersonal communication provides the distribution of helpful information. Yet, it can also cause the distribution of information pollution. Information pollution can become massive with the intensified interaction between users and cause infodemic. The World Health Organization secs infodcmic as a global problem that needs to be tackled as much as the pandemic in terms of human health. It leads to information pollution and misleads people's attitudes towards the virus and vaccine. After the video titled "Plandemic: The I lidden Agenda Behind COVID-19" was published on social media platforms such as Youtube and Facebook on May 4, 2020, it spread rapidly and became one of the concrete cases of the infodemic. The video spread rapidly with the shares of the viewers in the two days it was broadcast and reached the number of views close to two million. It has drawn the masses into its magic circle by packaging the COVID-19 claims that are voiced by groups prone to conspiracy theories. Although the video was taken down in a slightly short time, the misinformation it contained remained in circulation globally through interactions on social media. In this research, the reflections of the Plandemic video in Turkey were determined by the content produced by the users on Twitter via the #Plandemi hashtag. The primary purpose of the research is to examine how false information is spread in social media by giving examples. Stuctured as a case study, this research conducts network analysis and content analysis to the posts with the #Plandemi hashtag on Twitter. As a result of the network analysis, power-law distribution has been determined in the interaction between users and message production. As a result of the content analysis, it was determined that there was a contextual overlap with the Plandemic video, especially in the theme of insecurity in the health system. In this respect, the study represents an example of the global fluidity of the infodemic while describing how disinformation is spreading in Turkey in the context of COVID-19.

Research on the Influence of Information Diffusion on the Transmission of the Novel Coronavirus (COVID-19).

With the rapid development of the Mobile Internet in China, epidemic information is real-time and holographic, and the role of information diffusion in epidemic control is increasingly prominent. At the same time, the publicity of all kinds of big data also provides the possibility to explore the impact of media information diffusion on disease transmission. We explored the mechanism of the influence of information diffusion on the transmission of COVID-19, developed a model of the interaction between information diffusion and disease transmission based on the Susceptible-Infected-Recovered (SIR) model, and conducted an empirical test by using econometric methods. The benchmark result showed that there was a significant negative correlation between the information diffusion and the transmission of COVID-19. The result of robust test showed that the diffusion of both epidemic information and protection information hindered the further transmission of the epidemic. Heterogeneity test results showed that the effect of epidemic information on the suppression of COVID-19 is more significant in cities with weak epidemic control capabilities and higher Internet development levels.

Measuring user engagement with low credibility media sources in a controversial online debate.

We quantify social media user engagement with low-credibility online news media sources using a simple and intuitive methodology, that we showcase with an empirical case study of the Twitter debate on immigration in Italy. By assigning the Twitter users an Untrustworthiness (U) score based on how frequently they engage with unreliable media outlets and cross-checking it with a qualitative political annotation of the communities, we show that such information consumption is not equally distributed across the Twitter users. Indeed, we identify clusters characterised by a very high presence of accounts that frequently share content from less reliable news sources. The users with high U are more keen to interact with bot-like accounts that tend to inject more unreliable content into the network and to retweet that content. Thus, our methodology applied to this real-world network provides evidence, in an easy and straightforward way, that there is strong interplay between accounts that display higher bot-like activity and users more focused on news from unreliable sources and that this influences the diffusion of this information across the network.

Comparation and Sentiment Analysis Method of Chinese and Western Media's COVID-19 Reports on Various Countries

Media plays an important role in the information society and therefore, it should be fair, just, and objective. Media has been influencing the public's cognition of COVID-19 and their views of the actions taken by the government of different countries in 2020. After analyzing more than 260 thousand reports collected from search engines and published by both the Chinese and Western media about 10 countries, we found that Western media has prejudice when reporting the epidemic in China, and there exist obvious abnormal features when they reporting the epidemic in the United States. In addition, compared to Western media, Chinese media are more consistent and objective with the actual development of the epidemic in different countries. © 2022, Science Press. All right reserved.

Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users

In this paper, a homogeneous continuous time Markov chain (CTMC) is used to model information diffusion or dissemination, also to determine influencers on Twitter dynamically. The tweeting process can be modeled with a homogeneous CTMC since the properties of Markov chains are fulfilled. In this case, the tweets that are received by followers only depend on the tweets from the previous followers. Knowledge Discovery in Database (KDD) in Data Mining is used to be research methodology including pre-processing, data mining process using homogeneous CTMC, and post-pro-cessing to get the influencers using visualization that predicts the number of affected users. We assume the number of affected users follows a logarithmic function. Our study examines the Indonesian Twitter data users with tweets about covid19 vaccination resulted in dynamic influencer rankings over time. From these results, it can also be seen that the users with the highest number of followers are not necessarily the top influencer. © 2022, Growing Science. All rights reserved.

A RED-BET Method to Improve the Information Diffusion on Social Networks

Information diffusion in the social network has been widely used in many fields today, from online marketing, e-government campaigns to predicting large social events. Some study focuses on how to discover a method to accelerate the parameter calculation for the information diffusion forecast in order to improve the efficiency of the information diffusion problem. The Betweenness Centrality is a significant indicator to identify the important people on social networks that should be aimed to maximize information diffusion. Thus, in this paper, we propose the RED-BET method to improve the information diffusion on social networks by a hybrid approach that allows to quickly determine the nodes having high Betweenness Centrality. Our main idea in the proposed method combines both the graph reduction and parallelization of the Betweenness Centrality calculation. Experimental results with the currently popular large datasets of SNAP and Animer have demonstrated that our proposed method improves the performance from 1.2 to 1.41 times compared to the TeexGraph toolkit, from 1.76 to 2.55 times than the NetworKit, and from 1.05 to 1.1 times in comparison with the bigGraph toolkit.

A Study on Emotional Analysis Focusing on Onomatopoeia Used on SNS for the COVID-19

COVID-19 has dramatically changed the social situation in Japan. Along with the change in the real society, COVID-19 also changes the usage of social media. This study reports on findings from an analysis of onomatopoeia appears in posts on social media regarding COVID-19 to see how it has affected people's emotion. We analyzed the frequency of appearance of onomatopoeias expressing specific emotions according to the time variation, the relation between major events such as the declaration of state of emergency, and changes of co-occurrence words for the onomatopoeias. As a result of analysis, we found that the frequencies and degree of variation of onomatopoeias that belong to the same emotion group are complexly associated. The analysis results on co-occurrence words and frequency shift by events suggest that the cause of the change in emotion was different even for the onomatopoeia expressing the same emotion. The long-term emotional changes marked the peak in June 2020 during the second COVID-19 outbreak in Japan, rather than the first outbreak occurred in April 2020. At this time, as the number of infected people increased, the frequency of the use of the onomatopoeias also tended to increase. From the first case of COVID-19 in Japan (Jan 2019) to the second outbreak (Jun 2020), "anger "and "fear"were dominant emotions then they change to "peace of mind"during the second peak to the third outbreak (Nov 2020), and finally become "disgust". © 2021 ACM.

What makes an online help-seeking message go far during the COVID-19 crisis in mainland China? A multilevel regression analysis.

Various studies have explored the underlying mechanisms that enhance the overall reach of a support-seeking message on social media networks. However, little attention has been paid to an under-examined structural feature of help-seeking message diffusion, information diffusion depth, and how support-seeking messages can traverse vertically into social media networks to reach other users who are not directly connected to the help-seeker. Using the multilevel regression to analyze 705 help-seeking posts regarding COVID-19 on Sina Weibo, we examined sender, content, and environmental factors to investigate what makes help-seeking messages traverse deeply into social media networks. Results suggested that bandwagon cues, anger, instrumental appeal, and intermediate self-disclosure facilitate the diffusion depth of help-seeking messages. However, the effects of these factors were moderated by the epidemic severity. Implications of the findings on support-seeking behavior and narrative strategies on social media were also discussed.

From #jobsearch to #mask: Improving COVID-19 cascade prediction with spillover effects

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance hot information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decision to participate in diffusing certain information is still not studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures. Through our collected Twitter dataset, we validated the existence of this spillover effect. Building on the finding, we proposed extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effect significantly improves the state-of-the-art GNNs methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 related messages. © 2021 Owner/Author.

Exploring Spillover Effects for COVID-19 Cascade Prediction.

An information outbreak occurs on social media along with the COVID-19 pandemic and leads to an infodemic. Predicting the popularity of online content, known as cascade prediction, allows for not only catching in advance information that deserves attention, but also identifying false information that will widely spread and require quick response to mitigate its negative impact. Among the various information diffusion patterns leveraged in previous works, the spillover effect of the information exposed to users on their decisions to participate in diffusing certain information has not been studied. In this paper, we focus on the diffusion of information related to COVID-19 preventive measures due to its special role in consolidating public efforts to slow down the spread of the virus. Through our collected Twitter dataset, we validate the existence of the spillover effects. Building on this finding, we propose extensions to three cascade prediction methods based on Graph Neural Networks (GNNs). Experiments conducted on our dataset demonstrated that the use of the identified spillover effects significantly improves the state-of-the-art GNN methods in predicting the popularity of not only preventive measure messages, but also other COVID-19 messages.

Effects and Mechanism of Weibo’s Negative Emotions on Covid-19 Related Retweets Based on Big Data Collection Technology

This study based on big data collection technology with Weibo contents to reveal the relationship between negative emotion and information diffusion during Covid-19 pandemic. Specifically, focusing on how negative emotion influences the number of reposts (retweets). From January 23 to February 7 2020, 176,934 Weibo posts collected with the keyword “novel coronavirus pneumonia”. Negative binomial regression method is applied to construct an empirical model between negative emotion and retweets. Regression results demonstrated that there is not a single linear relationship between the two, when the negative emotion exceed a certain level, retweets would decrease instead. Our results implicate risk communication can be manipulated by controlling the negative intensity in social media contents, even under extreme risk. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Why Do Citizens Share COVID-19 Fact-Checks Posted by Chinese Government Social Media Accounts? The Elaboration Likelihood Model.

Widespread misinformation about COVID-19 poses a significant threat to citizens long-term health and the combating of the disease. To fight the spread of misinformation, Chinese governments have used official social media accounts to participate in fact-checking activities. This study aims to investigate why citizens share fact-checks about COVID-19 and how to promote this activity. Based on the elaboration likelihood model, we explore the effects of peripheral cues (social media capital, social media strategy, media richness, and source credibility) and central cues (content theme and content importance) on the number of shares of fact-checks posted by official Chinese Government social media accounts. In total, 820 COVID-19 fact-checks from 413 Chinese Government Sina Weibo accounts were obtained and evaluated. Results show that both peripheral and central cues play important roles in the sharing of fact-checks. For peripheral cues, social media capital and media richness significantly promote the number of shares. Compared with the push strategy, both the pull strategy and networking strategy facilitate greater fact-check sharing. Fact-checks posted by Central Government social media accounts receive more shares than local government accounts. For central cues, content importance positively predicts the number of shares. In comparison to fact-checks about the latest COVID-19 news, government actions received fewer shares, while social conditions received more shares.