Modeling the Contributions of Participator, Content, and Network to Topic Duration in Online Social Group

IF 4.5 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

IEEE Transactions on Computational Social Systems Pub Date : 2024-09-24 DOI:10.1109/TCSS.2024.3414586

Guoshuai Zhang;Jiaji Wu;Gwanggil Jeon;Penghui Wang;Yuan Chen;Yuhui Wang;Mingzhou Tan

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引用次数: 0

Abstract

As a common phenomenon that often appears on social platforms, news sites, and community forums, topics have played an irreplaceable role in public opinion and social governance. Meanwhile, people's daily lives are increasingly dependent on the breeding, transformation, and attenuation of hot topics. This article aims to discuss the problem about topic duration, that is, what are the principle factors that affect topic duration? Why do some topics survive longer and even generate subtopics, while other topics disappear rapidly? To answer these questions, we innovatively use 104 121 alliance chat content in Nova Empire II from July 2023 to December 2023 as a case study. Dynamic topics trajectories are first obtained from a novel multilevel association model. Then, a potential factors system based on the dimensions of topic properties, topic users, and social network is established to quantitatively evaluate the influence for different factors. Experimental results from a robust statistical analysis framework demonstrate that higher topic discussion intensity, more content from opinion leader, faster information diffusion, and closer intertopic correlations will significantly improve the topic duration. Finally, a series of strategies are proposed to promote the design of social system applications from the perspectives of online social group.

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在线社交群体中参与者、内容和网络对话题持续时间的贡献建模

话题作为一种经常出现在社交平台、新闻网站和社区论坛上的普遍现象，在舆论和社会治理中发挥着不可替代的作用。与此同时，人们的日常生活越来越依赖于热点话题的滋生、转化和衰减。本文旨在探讨话题持续时间的问题，即影响话题持续时间的主要因素是什么？为什么有些话题存活时间更长，甚至产生子话题，而其他话题却消失得很快？为了回答这些问题，我们创新性地以《新星帝国II》中2023年7月至2023年12月的104121个联盟聊天内容为例进行了研究。首先从一种新的多层关联模型中获得动态主题轨迹。然后，建立基于话题属性、话题用户和社交网络维度的潜在因素体系，定量评价不同因素的影响。基于稳健统计分析框架的实验结果表明，话题讨论强度越高、意见领袖的内容越多、信息扩散速度越快、话题间相关性越强，话题持续时间越长。最后，从网络社交群体的角度提出了一系列促进社交系统应用设计的策略。

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

IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)

CiteScore

10.00

自引率

20.00%

发文量

316

期刊介绍： IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.