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This paper aims to investigate the impacts of rumors' information characteristics on people's believing and spreading of rumors online.

This study employed a mixed-methods approach by combining qualitative and quantitative methods. In study 1, the authors explored different types of rumors and their information source characteristics through qualitative research. In study 2, the authors utilized the findings from study 1 to develop an empirical model to verify the impact of these characteristics on the public's behaviors of believing and spreading rumors by content analysis and quantitative research.

The results show that five information source characteristics – credibility, professionalism, attractiveness, mystery and concreteness – influence the spreading effect of different types of rumors.

This study contributes to rumor spreading research by deepening the theory of information source characteristics and adding to the emerging literature on the COVID-19 pandemic.

Insights from this research offer important practical implications for policymakers and online-platform operators by highlighting how to suppress the spread of rumors, particularly those associated with COVID-19.

This research introduces the theory of information source characteristics into the field of rumor spreading and adopts a mixed-methods approach, taking COVID-19 rumors as a typical case, which provides a unique perspective for a deeper understanding of rumor spreading's antecedences.

The purpose of this paper is to propose a novel method to enhance the spread of messages in social networks by “Spreading Groups.” These sub-structures of highly connected accounts intentionally echo messages between the members of the subgroup at the early stages of a spread. This echoing further boosts the spread to regions substantially larger than the initial region. These spreading accounts can be actual humans or social bots.

The paper reveals an interesting anomaly in information cascades in Twitter and proposes the spreading group model that explains this anomaly. The model was tested using an agent-based simulation, real Twitter data and questionnaires.

The messages of few anonymous Twitter accounts spread on average more than well-known global financial media groups, such as The Wall Street Journal or Bloomberg. The spreading groups (also sometimes called BotNets) model provides an effective mechanism that can explain these findings.

Spreading groups are only one possible mechanism that can explain the effectiveness of spread of tweets from lesser known accounts. The implication of this work is in showing how spreading groups can be used as a mechanism to spread messages in social networks. The construction of spreading groups is rather technical and does not require using opinion leaders. Similar to the case of “Fake News,” we expect the topic of spreading groups and their aim to manipulate information to receive growing attention in public discussion.

While harnessing opinion leaders to spread messages is costly, constructing spreading groups is more technical and replicable. Spreading groups are an efficient method to amplify the spread of message in social networks.

With the blossoming of fake news, one might tend to assess the reliability of news by the number of users involved in its spread. This heuristic might be easily fooled by spreading groups. Furthermore, spreading groups consisting of a blend of human and computerized bots might be hard to detect. They can be used to manipulate financial markets or political campaigns.

The paper demonstrates an anomaly in Twitter that was not studied before. It proposes a novel approach to spreading messages in social networks. The methods presented in the paper are valuable for anyone interested in spreading messages or an agenda such as political actors or other agenda enthusiasts. While social bots have been widely studied, their synchronization to increase the spread is novel.

Under the new media environment, while enjoying the convenience brought by the propagation of public opinion information (referred to as public opinion), learning the evolution process of public opinion and strengthening the governance of the spreading of public opinion are of great significance to promoting economic development and maintaining social stability as well as effectively resisting the negative impact of its propagation.

Thinking about the results of empirical research and bibliometric analysis, this paper focused on introducing key factors such as information content, social strengthening effects, etc., from both internal and external levels, dynamically designed public opinion spreading rules and netizens' state transition probability. Subsequently, simulation experiments were conducted to discuss the spreading law of public opinion in two types of online social networks and to identify the key factors which influencing its evolution process. Based on the experimental results, the governance strategies for the propagation of negative public opinion were proposed finally.

The results show that compared with other factors, the propagation of public opinion depends more on the attributes of the information content itself. For the propagation of negative public opinion, on the one hand, the regulators should adopt flexible guidance strategy to establish a public opinion supervision mechanism and autonomous system with universal participation. On the other hand, they still need to adopt rigid governance strategy, focusing on the governance timing and netizens with higher network status to forestall the wide-diffusion of public opinion.

The research conclusions put forward the enlightenment for the governance of public opinion in management practice, and also provided decision-making reference for the regulators to reasonably respond to the propagation of public opinion.

Our research proposed a research framework for the discussion of public opinion propagation process and had important practical guiding significance for the governance of public opinion propagation.

COVID-19, a causative agent of the potentially fatal disease, has raised great global public health concern. Information spreading on the COVID-19 outbreak can strongly influence people behaviour in social media. This paper aims to question of information spreading on COVID-19 outbreak are addressed with a massive data analysis on Twitter from a multidimensional perspective.

The evolutionary trend of user interaction and the network structure is analysed by social network analysis. A differential assessment on the topics evolving is provided by the method of text clustering. Visualization is further used to show different characteristics of user interaction networks and public opinion in different periods.

Information spreading in social media emerges from different characteristics during various periods. User interaction demonstrates multidimensional cross relations. The results interpret how people express their thoughts and detect topics people are most discussing in social media.

This study is mainly limited by the size of the data sets and the unicity of the social media. It is challenging to expand the data sets and choose multiple social media to cross-validate the findings of this study.

This paper aims to find the evolutionary trend of information spreading on the COVID-19 outbreak in social media, including user interaction and topical issues. The findings are of great importance to help government and related regulatory units to manage the dissemination of information on emergencies, in terms of early detection and prevention.

This aim of this paper is to elucidate rumor propagation on microblogs and to assess a system for collecting rumor information to prevent rumor-spreading.

We present a case study of how rumors spread on Twitter during a recent disaster situation, the Great East Japan earthquake of March 11, 2011, based on comparison to a normal situation. We specifically examine rumor disaffirmation because automatic rumor extraction is difficult. Extracting rumor-disaffirmation is easier than extracting the rumors themselves. We classify tweets in disaster situations, analyze tweets in disaster situations based on users' impressions and compare the spread of rumor tweets in a disaster situation to that in a normal situation.

The analysis results showed the following characteristics of rumors in a disaster situation. The information transmission is 74.9 per cent, representing the greatest number of tweets in our data set. Rumor tweets give users strong behavioral facilitation, make them feel negative and foment disorder. Rumors of a normal situation spread through many hierarchies but the rumors of disaster situations are two or three hierarchies, which means that the rumor spreading style differs in disaster situations and in normal situations.

The originality of this paper is to target rumors on Twitter and to analyze rumor characteristics by multiple aspects using not only rumor-tweets but also disaffirmation-tweets as an investigation object.

In modern society, considering the multi-channel of public opinion information (public opinion) propagation and its strong influence on social development, it is necessary to study its propagation law and discuss the intervention strategy in online social networks (OSN).

First, a conceptual model of double-layer OSN was constructed according to their structural characteristics. Then, a cross-network propagation model of public opinion in double-layer OSN was proposed and discussed its spreading characteristics through numerical simulations. Finally, the control strategy of public opinion, especially the timing and intensity of intervention were discussed.

The results show that the double-layer OSN promotes the propagation of public opinion, and the propagation of public opinion in double-layer OSN has the characteristics of that in two single-layer OSN. Compared with the intervention intensity, the regulator should give the priority to the timing of intervention and try to intervene in the early stage of public opinion propagation.

This study may help the regulators to respond to the propagation of public opinion in OSN more actively and reasonably.

This research has a deep comprehension of the cross-network propagation rules of public opinion and manages the propagation of public opinion.

In recent years, the social impact of misinformation has intensified. The purpose of this study is to clarify the mechanism by which misinformation spreads in society.

Testing the following two hypotheses by a logit model analysis of survey data using actual fact-checked COVID-19 vaccine and political misinformation: people who believe that some misinformation is true are more likely to spread it than those who do not believe in its truthfulness; people with lower media and information literacy are more likely to spread misinformation than people with higher media and information literacy.

The two hypotheses are supported, and the trend was generally robust regardless of the method, whether the means of diffusion was social media or direct conversation.

The authors derived the following four implications from the results: governments need to further promote media information literacy education; platform service providers should consider mechanisms to facilitate the spread and display of posts by people who are aware of misinformation; fact-checking should be further promoted; people should acquire information based on the assumption that people who believe in some misinformation tend to spread it more.

First, it quantitatively clarifies the relationship between misinformation, true/false judgements and dissemination behaviour. Second, it quantitatively clarifies the relationship between literacy and misinformation dissemination behaviour. Third, it conducts a comprehensive analysis of diffusion behaviours, including those outside of social media.

The study aims to investigate the predictors of engaging in combat against the spread of misinformation and disinformation online, and of actively sharing disinformation by users. The study advances an understanding of user active engagement with disinformation as political participation, especially linked to violent activism, in alignment with the view of disinformation as political weapon.

A survey of 502 Israeli internet users inquired into respondents' political participation, trust and orientation, definitions and perceptions of “Fake News,” and previous engagement in sharing misinformation disinformation items, combating or intention to combat against the spread of disinformation.

In addition to identifying predictors for each practice, the findings indicate that sharing and combating against disinformation are closely linked. They are also all directly linked to political participation of various kinds. Most interestingly, working for a political party significantly correlates with knowingly sharing disinformation items, and participating in illegal or violent political activities significantly correlates with knowingly sharing and actively participating in combat against disinformation.

The spread of disinformation online and its implications has received much scholarly as well as public attention in recent years. However, the characteristics of individual users who share or combat against the spread of disinformation online, as forms of political participation, have not been examined. This study fills this gap by inquiring into such practices and the behaviors, perceptions and demographics that predict them.

The purpose of this paper is to investigate the social media application and the spread of COVID-19 infodemic in Nigeria.

A descriptive survey research design was used for this study. A total of 1,200 social media users, regardless of their professions, were randomly selected for the study betweenmid-June and July 2020. Stratified and purposive sampling techniques were used for this study. The questionnaire was designed using Google form and administered using WhatsApp and Telegram to social media users above 18 years old in Nigeria. The data generated was analyzed using descriptive (frequency count) and inferential (mean) statistics, and was presented in tables.

It was found that people make use of social media during COVID-19 pandemic for diverse reasons such as listening to announcement to be informed, knowing the necessary measures to take by those infected and spreading up-to-date information on the pandemic. Social media tools were highly used during the COVID-19 pandemic, especially WhatsApp and Zoom. Findings reflected that misinformation was spread on social media. This study also showed that the infodemic associated with COVID-19 is managed by confirming the source of the information before sharing it and trusting information from reliable sources.

The result of this research will contribute to the body of knowledge on social media application, fake news and the spread of COVID-19 infodemic in Nigeria and beyond.

Infodemic is a disaster in the health sector. The spread of infodemic is capable of misleading people, losing trust in government, health providers and health regulatory authorities. This study will help social media users to know how to properly manage social media infodemic during a pandemic or any health-related situations.

This study is novel as it approaches fake news from a COVID-19 perspective. Very few articles emanate from the developing countries in this area. This was because most of the narrative around fake news previously centered around the Western occurrences such as the Iraqi invasion by the USA, the US presidential elections and BREXIT. COVID-19 has demonstrated that the developing world is not immune from fake news as well. This study, therefore, assessed the management of infodemic associated with COVID-19 in Nigeria.

The purpose of this study is to examine the dark side of instant messaging from the technological and societal perspectives.

With the help of literature review and different theories, a model has been developed conceptually. Later the model has been validated using statistical method. The authors have used 304 responses from the survey method, and this sample has been used to statistically validate the conceptual model.

This paper has been able to explicitly investigate and identify how different instant messaging platforms such as WhatsApp, WeChat in the form of electronic word of mouth (e-WOM) are contributing toward increase of mob lynching cases. The paper also highlights the important to have effective and enforceable regulation to regulate instant messaging services to the citizens.

The findings cannot be generalized as the data is collected from India only. Moreover, the study is cross-sectional in nature. To get the comprehensive results, a longitudinal study needs to be conducted. This study considered seven constructs with one moderator. Having more predictors with other boundary conditions might have increased the explanative power of the model.

Instant messaging platforms such as WhatsApp, WeChat and so on are disseminating quick unverified information to the common people. This information sometimes is disseminated in inappropriate and exaggerated forms. This makes the instant messaging (WhatsApp) users' sentiment readily heated in some cases. They take such an action as mob lynching. This study determines the predictors of mob lynching along with the moderator impact of instant messaging in the society.

There are only a few studies those have explored the dark side of instant messaging. The proposed theoretical model is a unique model, which shows the predictors of mob lynching along with the negative consequences of the instant messaging (WhatsApp) in the society. From this perspective, this study can be considered as a unique study.