An online multi-dimensional opinion dynamic model with misinformation diffusion in emergency events

Liu, Mengmeng; Rong, Lili

doi:10.1177/0165551520977430

Cited by 5 publications

(2 citation statements)

References 42 publications

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“…Information alienation was highly correlated with the emergence of polarized subnetworks. Therefore, it is important to share information about a given topic of interest to public opinion as early as possible, especially during a CRIS [94]. However, this needs to be done carefully to avoid a scenario of inconsistent messaging in case the results need to be reviewed later.…”

Section: Opinion Dynamics Dimensionmentioning

confidence: 99%

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Altoe,

Moreira,

Pinto

et al. 2024

Human Behavior and Emerging Technologies

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Fake news has been linked to the rise of psychological disorders, the increased disbelief in science, and the erosion of democracy and freedom of speech. Online social networks are arguably the main vehicle of fake news spread. Educating online users with explanations is one way of preventing this spread. Understanding how online belief is formed and changed may offer a roadmap for such education. The literature includes surveys addressing online opinion formation and polarization; however, they usually address a single domain, such as politics, online marketing, health, and education, and do not make online belief change their primary focus. Unlike other studies, this work is the first to present a cross-domain systematic literature review of user studies, methodologies, and opinion model dimensions. It also includes the orthogonal polarization dimension, focusing on online belief change. We include peer-reviewed works published in 2020 and later found in four relevant scientific databases, excluding theoretical publications that did not offer validation through dataset experimentation or simulation. Bibliometric networks were constructed for better visualization, leading to the organization of the papers that passed the review criteria into a comprehensive taxonomy. Our findings show that a person’s individuality is the most significant influential force in online belief change. We show that online arguments that balance facts with emotionally evoking content are more efficient in changing their beliefs. Polarization was shown to be cross-correlated among multiple subjects, with politics being the central polarization pole. Polarized online networks start as networks with high opinion segregation, evolve into subnetworks of consensus, and achieve polarization around social network influencers. Trust in the information source was demonstrated to be the chief psychological construct that drives online users to polarization. This shows that changing the beliefs of influencers may create a positive snowball effect in changing the beliefs of polarized online social network users. These findings lay the groundwork for further research on using personalized explanations to reduce the harmful effects of online fake news on social networks.

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Section: Opinion Dynamics Dimensionmentioning

confidence: 99%

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Altoe,

Moreira,

Pinto

et al. 2024

Human Behavior and Emerging Technologies

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“…Besides taking two extremes (true or false), one can envision a continuous opinion space but the actions taken are discrete [9][10][11]. Liu et al inferred the diffusion rate of misinformation and whether agents can debunk misinformation by calculating the densities of users with different opinions [12,13].…”

Section: Introductionmentioning

confidence: 99%

Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study

Wu,

Guo

2024

Entropy

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Misinformation has posed significant threats to all aspects of people’s lives. One of the most active areas of research in misinformation examines how individuals are misinformed. In this paper, we study how and to what extent agents are misinformed in an extended bounded confidence model, which consists of three parts: (i) online selective neighbors whose opinions differ from their own but not by more than a certain confidence level; (ii) offline neighbors, in a Watts–Strogatz small-world network, whom an agent has to communicate with even though their opinions are far different from their own; and (iii) a Bayesian analysis. Furthermore, we introduce two types of epistemically irresponsible agents: agents who hide their honest opinions and focus on disseminating misinformation and agents who ignore the messages received and follow the crowd mindlessly. Simulations show that, in an environment with only online selective neighbors, the misinforming is more successful with broader confidence intervals. Having offline neighbors contributes to being cautious of misinformation, while employing a Bayesian analysis helps in discovering the truth. Moreover, the agents who are only willing to listen to the majority, regardless of the truth, unwittingly help to bring about the success of misinformation attempts, and they themselves are, of course, misled to a greater extent.

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Research on the dynamic mechanism of group emotional expression in the crisis

Luo

et al. 2022

Telematics and Informatics

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An online multi-dimensional opinion dynamic model with misinformation diffusion in emergency events

Cited by 5 publications

References 42 publications

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Online Fake News Opinion Spread and Belief Change: A Systematic Review

Modeling Misinformation Spread in a Bounded Confidence Model: A Simulation Study

Research on the dynamic mechanism of group emotional expression in the crisis

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