A Novel Agent-Based Rumor Spreading Model in Twitter

Serrano, Emilio; Iglesias, Carlos Á.; Garijo, Mercedes Ruiz

doi:10.1145/2740908.2742466

Cited by 27 publications

(10 citation statements)

References 17 publications

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“…What was once spread locally can rapidly become global, with ideas no longer confined or delayed by geography. This has generated a series of studies of information diffusion (Serrano et al, 2015), rumour spread (He et al, 2015), and consequent behavioural changes (Salathé and Khandelwal, 2011;Wakamiya et al, 2016). These generally employ sophisticated modelling and simulation techniques to identify the rumour propagation dynamics.…”

Section: Misinformation and Health: Gaps In The Evidence Basementioning

confidence: 99%

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Wang

McKee

Torbica

et al. 2019

Social Science & Medicine

1,164

806

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A B S T R A C TContemporary commentators describe the current period as "an era of fake news" in which misinformation, generated intentionally or unintentionally, spreads rapidly. Although affecting all areas of life, it poses particular problems in the health arena, where it can delay or prevent effective care, in some cases threatening the lives of individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientific medicine, the internet, by allowing instantaneous communication and powerful amplification has brought about a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientific information, which may be difficult to comprehend and even dull, is easily crowded out by sensationalized news. In order to uncover the current evidence and better understand the mechanism of misinformation spread, we report a systematic review of the nature and potential drivers of health-related misinformation. We searched PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis. Overall, we observe an increasing trend in published articles on health-related misinformation and the role of social media in its propagation. The most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation analysis revealed potential for greater collaboration across fields. Most studies employed content analysis, social network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine susceptibility of different sociodemographic groups to misinformation and understand the role of belief systems on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify effective and tailored interventions to counter the spread of health-related misinformation online.

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Section: Misinformation and Health: Gaps In The Evidence Basementioning

confidence: 99%

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Wang

McKee

Torbica

et al. 2019

Social Science & Medicine

1,164

806

View full text Add to dashboard Cite

show abstract

“…In [4], the authors model the information diffusion using agents with well-defined states, similar to the epidemic SIR (Susceptible, Infected, Recovered) model and use two datasets from the Twitter social network to compare the efficiency of the proposed model regarding the realistic simulation of the diffusion. The model introduced in this paper is based on the fact that those users who may know that a rumor is false, will not spread messages that deny these rumors.…”

Section: Related Workmentioning

confidence: 99%

A novel information diffusion model based on psychosocial factors with automatic parameter learning

Floria

Leon

2021

COMSIS J

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Online social networks are the main choice of people to maintain their social relationships and share information or opinions. Estimating the actions of a user is not trivial because an individual can act spontaneously or be influenced by external factors. In this paper we propose a novel model for imitating the evolution of the information diffusion in a network as well as possible. Each individual is modeled as a node with two factors (psychological and sociological) that control its probabilistic transmission of information. The psychological factor refers to the node?s preference for the topic discussed, i.e. the information diffused. The sociological factor takes into account the influence of the neighbors? activity on the node, i.e. the gregarious behavior. A genetic algorithm is used to automatically tune the parameters of the model in order to fit the evolution of information diffusion observed in two real-world datasets with three topics. The reproduced diffusions show that the proposed model imitates the real diffusions very well.

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“…Though the integration of the two approaches has been increasingly discussed [5,6], there are only few studies so far that have actually attempted to combine these [7–11]. None of them, however, studies polarization and integrates the unstructured data analysis comprehensively with the computational model.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we present a novel approach, in which we combine rich social media data with the power of methods of statistical physics, to study political opinion polarization mechanisms, seeking to understand what mechanisms turned Ukraine into an irreconcilably polarized state. Though the integration of the two approaches has been increasingly discussed [5,6], there are only few studies so far that have actually attempted to combine these [7][8][9][10][11]. None of them, however, studies polarization and integrates the unstructured data analysis comprehensively with the computational model.…”

Section: Introductionmentioning

confidence: 99%

Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data

et al. 2018

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Multiple countries have recently experienced extreme political polarization, which, in some cases, led to escalation of hate crime, violence and political instability. Besides the much discussed presidential elections in the USA and France, Britain's Brexit vote and Turkish constitutional referendum showed signs of extreme polarization. Among the countries affected, Ukraine faced some of the gravest consequences. In an attempt to understand the mechanisms of these phenomena, we here combine social media analysis with agent-based modelling of opinion dynamics, targeting Ukraine's crisis of 2014. We use Twitter data to quantify changes in the opinion divide and parametrize an extended bounded confidence XY model, which provides a spatio-temporal description of the polarization dynamics. We demonstrate that the level of emotional intensity is a major driving force for polarization that can lead to a spontaneous onset of collective behaviour at a certain degree of homophily and conformity. We find that the critical level of emotional intensity corresponds to a polarization transition, marked by a sudden increase in the degree of involvement and in the opinion bimodality.

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A Novel Agent-Based Rumor Spreading Model in Twitter

Cited by 27 publications

References 17 publications

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

A novel information diffusion model based on psychosocial factors with automatic parameter learning

Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data

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