scholarly journals The Spreading of Information in Online Social Networks through Cellular Automata

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yuda Wang ◽  
Gang Li
Keyword(s):  
Social Networks ◽  
Cellular Automata ◽  
Infection Rate ◽  
Online Social Networks ◽  
Information Dissemination ◽  
Epidemic Models ◽  
Computational Method ◽  
Online Information ◽  
Information Dynamics ◽  
Information Spreading

Epidemic dynamics in complex networks have been extensively studied. Due to the similarity between information and disease spreading, most studies on information dynamics use epidemic models and merely consider the characteristics of online social networks and individual’s cognitive. In this paper, we propose an online social networks information spreading (OSIS) model combining epidemic models and individual’s cognitive psychology. Then we design a cellular automata (CA) method to provide a computational method for OSIS. Finally, we use OSIS and CA to simulate the spreading and evolution of information in online social networks. The experimental results indicate that OSIS is effective. Firstly, individual’s cognition affects online information spreading. When infection rate is low, it prevents the spreading, whereas when infection rate is sufficiently high, it promotes transmission. Secondly, the explosion of online social network scale and the convenience of we-media greatly increase the ability of information dissemination. Lastly, the demise of information is affected by both time and heat decay rather than probability. We believe that these findings are in the right direction for perceiving information spreading in online social networks and useful for public management policymakers seeking to design efficient programs.

scholarly journals Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

2021 ◽  
Vol 13 (3) ◽  
pp. 76
Author(s):  
Quintino Francesco Lotito ◽  
Davide Zanella ◽  
Paolo Casari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Simulation Models ◽  
Epidemic Models ◽  
Time Varying ◽  
Fake News ◽  
Network Access ◽  
Spreading Process ◽  
Network Topologies ◽  
Access Patterns

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

The model of information dissemination on social networks under epidemic-related panic

2021 ◽  
Author(s):  
Fuzhong Nian ◽  
Xin Guo ◽  
JinZhou Li
Keyword(s):  
Social Networks ◽  
Individual Differences ◽  
Infection Rate ◽  
Information Dissemination ◽  
Self Regulation ◽  
Cumulative Effect ◽  
Herd Effect ◽  
Collective Wisdom ◽  
Leading Role

Inspired by infectious disease dynamics and modern psychology, this paper aims at constructing a multi-dimensional function to get the model of information dissemination on social networks under epidemic-related panic base on the characteristics of individual differences and global characteristics, like emotional cumulative effect, herd effect, time-sensitive decline effect, cognitive level, intimacy, personal influence, etc. The results show that the psychological effect has a significant effect on the increase of the spread of panic news; When netizens are in an emotional atmosphere, their emotional self-regulation ability is limited; when the infection rate is relatively low, the characteristics of individual differences play a leading role in affecting the spreading process. When the infection rate is high enough, the herd effect and emotional cumulative effect play a major role in promoting information dissemination; In a society with a higher rate of emotional contact, it is easier to form a kind of collective wisdom, which can help the collective quickly identify rumors. Moreover, in this kind of society, the role of opinion leaders is limited, and timely refutation of rumors can significantly reduce the spread of panic news.

The Unified Model for Acceptance and Use of Health Information on Online Social Networks

2016 ◽  
Vol 7 (1) ◽  
pp. 31-47 ◽  
Author(s):  
Waransanang Boontarig ◽  
Borworn Papasratorn ◽  
Wichian Chutimaskul
Keyword(s):  
Social Networks ◽  
Health Information ◽  
Online Social Networks ◽  
Information Dissemination ◽  
Source Credibility ◽  
Unified Model ◽  
Dissemination Strategy ◽  
Facilitating Conditions ◽  
Emotional Value ◽  
Beliefs And Attitudes

Online social networks provide a novel opportunity to improve public health through effective health information dissemination. Developing a dissemination strategy, however, requires an understanding of individuals' beliefs and attitudes about using both the technology and information. Previous research has focused primarily on either technology adoption or information adoption behaviors. This study aims to bridge the gap by developing a unified model of acceptance and use of information technology for predicting intention to use health information through online social networks. Empirical results show that Performance Expectancy, Facilitating Conditions, Perceived Emotional Value, Trust, Relevance, Accuracy, Understandability, and Source Credibility influence the adoption behavior. Also, individuals tend to accept health information regardless of their attitudes toward the communication channel.

A Study on Online Social Networks Theme Semantic Computing Model

2016 ◽  
Vol 13 (4) ◽  
pp. 67-90
Author(s):  
Chen Fu ◽  
Xu Yuemei ◽  
Ni Yihan
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Semantic Analysis ◽  
Great Influence ◽  
Semantic Distance ◽  
Spreading Rate ◽  
Online Information ◽  
Network Information ◽  
Huge Impact ◽  
Semantic Computing

The widespread use of Mobile Intelligent Terminals and ubiquitous access to networks has enabled online information sources including Weibo and Wechat to bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. The highly real-time content, fission-like spreading rate and enormous public opinion guiding forces created in this process will cast great influence on the society. Thus, semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Through the numerization of text, the calculation and comparison of semantic distance, the classification of nodes and the discovery of community can be realized. In this article, semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies. Then, huge quantities of Sina Weibo contents are collected to verify the model and algorithm put forward in this article. In the end, outlooks for future jobs are provided.

scholarly journals Mutual Influence of Users Credibility and News Spreading in Online Social Networks

2021 ◽  
Vol 13 (5) ◽  
pp. 107
Author(s):  
Vincenza Carchiolo ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni ◽  
Marialaura Previti
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Mutual Influence ◽  
Collective Intelligence ◽  
Online Information ◽  
Fake News ◽  
Potential Growth ◽  
Multiplex Network ◽  
Time News ◽  
Information Providers

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

scholarly journals COVID-19 fake news diffusion across Latin America

2021 ◽  
Author(s):  
Wilson Ceron ◽  
Gabriela Gruszynski Sanseverino ◽  
Mathias-Felipe de-Lima-Santos ◽  
Marcos G. Quiles
Keyword(s):  
Latin America ◽  
Information Dissemination ◽  
National Level ◽  
Application Programming Interface ◽  
Computational Method ◽  
Online Information ◽  
Fake News ◽  
False Information ◽  
Health Crisis ◽  
Fact Checking

Abstract Fact-checking verifies a multitude of claims and remains a promising solution to fight fake news. The spread of rumors, hoaxes, and conspiracy theories online is evident in times of crisis, when fake news ramped up across platforms, increasing fear and confusion amongst the population as seen in the COVID-19 pandemic. This article explores fact-checking initiatives in Latin America, using an original Markov-based computational method to cluster topics on tweets and identify their diffusion between different datasets. Drawing on a mixture of quantitative and qualitative methods, including time-series analysis, network analysis and in-depth close reading, our article proposes an in-depth tracing of COVID-related false information across the region, comparing if there is a pattern of behavior through the countries. We rely on the open Twitter application programming interface (API) connection to gather data from public accounts of the six major fact-checking agencies in Latin America, namely: Argentina ( Chequeado ), Brazil ( Agência Lupa ), Chile ( Mala Espina Check ), Colombia ( Colombia Check from Consejo de Redacción ), Mexico ( El Sabueso from Animal Político ) and Venezuela ( Efecto Cocuyo ). In total, these profiles account for 102,379 tweets that were collected between January and July 2020. Our study offers insights into the dynamics of online information dissemination beyond the national level and demonstrates how politics intertwine with the health crisis in this period. Our method is capable of clustering topics in a period of overabundance of information, as we fight not only a pandemic but also an infodemic, evidentiating opportunities to understand and slow the spread of false information.

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