A model of opinion and propagation structure polarization in social media

Q1 Mathematics

Computational Social Networks Pub Date : 2020-01-09 DOI:10.1186/s40649-019-0076-z

Hafizh A. Prasetya, Tsuyoshi Murata

{"title":"A model of opinion and propagation structure polarization in social media","authors":"Hafizh A. Prasetya, Tsuyoshi Murata","doi":"10.1186/s40649-019-0076-z","DOIUrl":null,"url":null,"abstract":"The issue of polarization in online social media has been gaining attention in recent years amid the changing political landscapes of many parts of the world. Several studies empirically observed the existence of echo chambers in online social media, stimulating a slew of works that tries to model the phenomenon via opinion modeling. Here, we propose a model of opinion dynamics centered around the notion that opinion changes are invoked by news exposure. Our model comes with parameters for opinions and connection strength which are updated through news propagation. We simulate the propagation of multiple news under the model in synthetic networks and observe the evolution of the model’s parameters and the propagation structure induced. Unlike previous models, our model successfully exhibited not only polarization of opinion, but also segregated propagation structure. By analyzing the results of our simulations, we found that the formation probability of echo chambers is primarily connected to the news polarization. However, it is also affected by intolerance to dissimilar opinions and how quickly individuals update their opinions. Through simulations on Twitter networks, we found that the behavior of the model is reproducible across different network structure and sizes.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"42 7","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Social Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40649-019-0076-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}

引用次数: 33

Abstract

The issue of polarization in online social media has been gaining attention in recent years amid the changing political landscapes of many parts of the world. Several studies empirically observed the existence of echo chambers in online social media, stimulating a slew of works that tries to model the phenomenon via opinion modeling. Here, we propose a model of opinion dynamics centered around the notion that opinion changes are invoked by news exposure. Our model comes with parameters for opinions and connection strength which are updated through news propagation. We simulate the propagation of multiple news under the model in synthetic networks and observe the evolution of the model’s parameters and the propagation structure induced. Unlike previous models, our model successfully exhibited not only polarization of opinion, but also segregated propagation structure. By analyzing the results of our simulations, we found that the formation probability of echo chambers is primarily connected to the news polarization. However, it is also affected by intolerance to dissimilar opinions and how quickly individuals update their opinions. Through simulations on Twitter networks, we found that the behavior of the model is reproducible across different network structure and sizes.

查看原文本刊更多论文

社交媒体中观点与传播结构极化模型

近年来，随着世界许多地区政治格局的变化，在线社交媒体的两极分化问题越来越受到关注。几项研究从经验上观察到了在线社交媒体中回声室的存在，这激发了一系列试图通过意见建模来模拟这一现象的作品。在这里，我们提出了一个舆论动态模型，围绕着新闻曝光引发舆论变化的概念。我们的模型带有通过新闻传播更新的观点和连接强度参数。我们模拟了多个新闻在该模型下在合成网络中的传播，观察了模型参数的演化和诱导的传播结构。与以往的模型不同，我们的模型不仅成功地展示了观点的两极分化，而且还展示了隔离的传播结构。通过对模拟结果的分析，我们发现回波室的形成概率主要与新闻极化有关。然而，它也受到对不同意见的不容忍以及个人更新意见的速度的影响。通过对Twitter网络的模拟，我们发现该模型的行为在不同的网络结构和规模上是可重复的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.