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Disinformation on social media is a serious issue. This study examines the effects of disinformation on COVID-19 vaccination decision-making to understand how social media users make healthcare decisions when disinformation is presented in their social media feeds. It examines trust in post owners as a moderator on the relationship between information types (i.e. disinformation and factual information) and vaccination decision-making.

This study conducts a scenario-based web survey experiment to collect extensive survey data from social media users.

This study reveals that information types differently affect social media users' COVID-19 vaccination decision-making and finds a moderating effect of trust in post owners on the relationship between information types and vaccination decision-making. For those who have a high degree of trust in post owners, the effect of information types on vaccination decision-making becomes large. In contrast, information types do not affect the decision-making of those who have a very low degree of trust in post owners. Besides, identification and compliance are found to affect trust in post owners.

This study contributes to the literature on online disinformation and individual healthcare decision-making by demonstrating the effect of disinformation on vaccination decision-making and providing empirical evidence on how trust in post owners impacts the effects of information types on vaccination decision-making. This study focuses on trust in post owners, unlike prior studies that focus on trust in information or social media platforms.

Disinformation, false information designed with the intention to mislead, can significantly damage organizational operation and reputation, interfering with communication and relationship management in a wide breadth of risk and crisis contexts. Modern digital platforms and emerging technologies, including artificial intelligence (AI), introduce novel risks in crisis management (Guthrie and Rich, 2022). Disinformation literature in security and computer science has assessed how previously introduced technologies have affected disinformation, demanding a systematic and coordinated approach for sustainable counter-disinformation efforts. However, there is a lack of theory-driven, evidence-based research and practice in public relations that advises how organizations can effectively and proactively manage risks and crises driven by AI (Guthrie and Rich, 2022).

As a first step in closing this research-practice gap, the authors first synthesize theoretical and technical literature characterizing the effects of AI on disinformation. Upon this review, the authors propose a conceptual framework for disinformation response in the corporate sector that assesses (1) technologies affecting disinformation attacks and counterattacks and (2) how organizations can proactively prepare and equip communication teams to better protect businesses and stakeholders.

This research illustrates that future disinformation response efforts will not be able to rely solely on detection strategies, as AI-created content quality becomes more and more convincing (and ultimately, indistinguishable), and that future disinformation management efforts will need to rely on content influence rather than volume (due to emerging capabilities for automated production of disinformation). Built upon these fundamental, literature-driven characteristics, the framework provides organizations actor-level and content-level perspectives for influence and discusses their implications for disinformation management.

This research provides a theoretical basis and practitioner insights by anticipating how AI technologies will impact corporate disinformation attacks and outlining how companies can respond. The proposed framework provides a theory-driven, practical approach for effective, proactive disinformation management systems with the capacity and agility to detect risks and mitigate crises driven by evolving AI technologies. Together, this framework and the discussed strategies offer great value to forward-looking disinformation management efforts. Subsequent research can build upon this framework as AI technologies are deployed in disinformation campaigns, and practitioners can leverage this framework in the development of counter-disinformation efforts.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.

The purpose of this study is to explore the evolutionary path and stable strategy for the competitive dissemination between disinformation and knowledge on social media to provide effective solutions to curb the dissemination of disinformation and promote the spread of knowledge.

Based on the social capital (SC) theory, the benefit matrix is constructed and an evolutional game model is established in this paper. Through model solving and Matrix Laboratory (MATLAB) simulation, the factors that influence disinformation-believing users (DUs) and knowledge-believing users (KUs) to choose different strategies are analyzed.

The initial dissemination willingness, the disinformation infection probability, the knowledge infection probability and the knowledge penetration probability are proved to be crucial factors influencing the game equilibrium in the competitive dissemination process of disinformation and knowledge. Moreover, some countermeasures and recommendations for the governance of disinformation are proposed.

Currently most research interest lies in the disinformation dissemination model but ignores the interaction between disinformation and knowledge in the diffusion process. This study reveals the dynamic mechanism of social media users disseminating disinformation and knowledge and is expected to promote the formation of cleaner cyberspace.

The debate about tackling online misinformation and disinformation is not unique to the COVID-19 pandemic. However, the health crisis has elevated the danger of misleading information circulated on social media. Labelled as ‘infodemic’, the problem of COVID-19-related misinformation and disinformation prompted governments and social media platforms around the globe to impose various speech restrictions. The public and private policy frameworks aiming to curb the spread of the infodemic were adopted in a state of emergency and without proper scrutiny. Yet, they are shaping the future of content regulation, possibly affecting freedom of speech and other democratic values for years to come. This chapter looks at the key problematic aspects of actions taken by governments and social media platforms to address COVID-19-related misinformation and disinformation and discusses the possible long-term effects of these measures.

There are several connections between education and disinformation, including the association between years of schooling and vulnerability to unfounded hypothesizing. The purpose of this paper is to inquire into a competing explanation: political leaders might be exploring powerful teaching and learning strategies to disseminate agendas based on baseless assumptions, exploiting human’s tendency to generate robust theories even with incomplete or incorrect information.

The authors analyzed ten videos published online by a highly partisan YouTube channel. The footage contained informal encounters between former Brazilian President Jair Bolsonaro and supporters in front of his official residence. The team sought to answer two research questions: Do Mr Bolsonaro’s discursive moves include activators that lead the audience to understand that they are theorizing and reaching conclusions “on their own?” Does Mr Bolsonaro’s audience follow those clues and mention politically motivated hoaxes and conspiracy theories in their comments? This paper draws on perspectives from the field of educational research to investigate the mechanisms used by the president to shape public opinion.

The authors found evidence of the employment of elements akin to classroom discourse in the dialogues led by Mr Bolsonaro. Specifically, different types of rhetorical questions are present to a substantial extent in the data subset analyzed for this paper.

This work offers an alternative perspective to analyzing disinformation. By drawing from established literature from education research, this paper departs from facile explanations that take for granted the lack of intelligence of the audience. Conversely, it argues that popular, if not powerful, teaching and learning strategies might play an undesired role by shaping individuals’ cognitive processes to create robust, internally consistent theories about the world using flawed assumptions and incorrect “building blocks.”

Understanding individual susceptibility to political fake news is critical because fake news can target specific psychological profiles of vulnerable individuals. Consequently, this research examines five individual risk (i.e. susceptibility) and resilience (i.e. protective) factors, conspiracy mentality, patriotism, perceived threat to freedom, media literacy and concern for disinformation, to determine if they inform belief in political fake news and subsequently, to what degree belief impacts private engagement with political fake news.

Using a fictional political fake news stimulus, the authors conducted a deductive thematic analysis of 10 semi-structured interviews and an online survey of 722 United Kingdom (UK) citizens analysed using structural equation modelling.

Conspiracy mentality and patriotism were positively associated with belief in political fake news, while media literacy and concern for disinformation were negatively associated with belief in political fake news. Perceived threat to freedom was a strong theme in the qualitative data but had no statistical effect on belief in political fake news. Belief in political fake news was positively associated with further engagement with the fake news story, acting as a mediator in the model.

Distinct from previous research that focuses on partisanship and sharing behaviour, this research forwards a model underpinned by social identity theory to build an integrated understanding of political fake news belief. The results demonstrate that political identity motivations beyond partisanship are salient when examining individual susceptibility to political fake news and that belief in political fake news plays a core role in understanding subsequent private engagement with the story.