Impact of the Network Size and Frequency of Information Receipt on Polarization in Social Networks

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2024-06-21 DOI:10.1155/2024/4742401
Sudhakar Krishnarao, Shaja Arul Selvamani
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引用次数: 0

Abstract

Opinion Dynamics is an interdisciplinary area of research. Disciplines of Psychology and Sociology have proposed models of how individuals form opinions and how social interactions influence this process. Sociophysicists have interpreted the observed patterns in opinion formation in individuals as arising out of nonlinearity in the underlying process and helped shape the models. Agent-based modelling has offered an excellent platform to study the Opinion Dynamics of large groups of interacting individuals. In this paper, we take recent models in opinion formation in individuals. We recast them to create a proper dynamical system and inject the idea of clock time into evolving individuals’ opinions. Thus, the time interval between two successive receipts of new information (i.e., the frequency of information receipts) by an individual becomes a factor that can be studied. In recent decades, social media has continuously shrunk time intervals between receipt of new information (i.e., increased frequency of information receipts). The recast models are used to show that as the time interval between successive receipts of new information gets shorter and the number of individuals in one’s network becomes larger, the propensity for polarization of an individual increases. This explains how social media could have caused polarisation. We use the word “polarisation” to mean an individual’s inability to hold a neutral opinion. A polarisation number based on sociological parameters is proposed. Critical values of the polarisation number beyond which an individual is prone to polarization are identified. These critical values depend on psychological parameters. The reduced time intervals between the receipt of new information and an increase in the size of groups that interact can push the polarisation number to approach and cross the critical value and could have played a crucial role in polarising individuals and social groups. We also define the extent of polarisation as the width of the region around neutral within which an individual is unable to have an opinion. Reported results are for values of model parameters found in the literature. Our findings offer an opportunity to adjust model parameters to align with empirical evidence. The models of opinion formation in individuals and the understanding arrived at in this study will help study Opinion Dynamics with all its nuances and details on large social networks using agent-based​ modelling.

Abstract Image

网络规模和信息接收频率对社交网络两极分化的影响
舆论动力学是一个跨学科的研究领域。心理学和社会学学科提出了关于个人如何形成观点以及社会互动如何影响这一过程的模型。社会物理学家将观察到的个人意见形成模式解释为由基本过程中的非线性引起的,并帮助塑造了这些模型。基于代理的模型为研究由互动个体组成的大型群体的舆论动态提供了一个极好的平台。在本文中,我们采用了最新的个体意见形成模型。我们对其进行了重构,创建了一个适当的动态系统,并为个体意见的演变注入了时钟时间的概念。这样,个体连续两次接收新信息之间的时间间隔(即接收信息的频率)就成了一个可以研究的因素。近几十年来,社交媒体不断缩短接收新信息的时间间隔(即提高信息接收频率)。重铸模型表明,随着连续接收新信息的时间间隔越来越短,个人网络中的人数越来越多,个人的极化倾向就会增加。这就解释了社交媒体是如何造成两极分化的。我们使用 "极化 "一词来表示个人无法保持中立观点。我们提出了一个基于社会学参数的极化数。我们确定了极化数的临界值,超过这个临界值,个人就容易极化。这些临界值取决于心理参数。接收新信息的时间间隔缩短,以及互动群体规模的扩大,都会推动极化数接近或超过临界值,并可能在个人和社会群体的极化中发挥关键作用。我们还将两极分化的程度定义为个人无法发表意见的中性附近区域的宽度。报告的结果是文献中的模型参数值。我们的研究结果为调整模型参数以符合经验证据提供了机会。个人意见形成模型和本研究得出的认识将有助于使用基于代理的建模方法研究大型社交网络中的意见动态及其所有细微差别和细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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