Opinion Evolution with Information Quality of Public Person and Mass Acceptance Threshold.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-04-01 Epub Date: 2023-05-29 DOI:10.1089/big.2022.0271
Jing Wei, Yuguang Jia, Wanyi Tie, Hengmin Zhu, Weidong Huang
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引用次数: 0

Abstract

Public persons are nodes with high attention to public events, and their opinions can directly affect the development on events. However, because of rationality, the followers' acceptance to the public persons' opinions will depend on the information trait on public persons' opinions and own comprehension. To study how different opinions of the public persons guide different followers, we build an opinion dynamics model, which would provide a theoretical method for public opinion management. Based on the classical bounded confidence model, we extract the information quality variables and individual trust threshold and introduce them to construct our two-stage opinion evolution model. And then in the simulation experiments, we analyze the different effects of opinion information quality, opinion release time, and frequency on public opinion by adjusting the different parameters. Finally, we added a case to compare real data, the data from classical model simulation and the data from improved model simulation to verify the effectiveness on our model. The research found that the more sufficient the argument and the more moderate the attitude, the more likely to guide the public opinion. If public person holds different opinions and different information quality, he should choose different time to present his opinion to achieve ideal guide effect. When public person holds neutral opinion and the information quality is relatively general, he/she can intervene in public opinion as soon as possible to control final public opinion; when public person holds extreme opinion and the information quality is relatively high, he/she can choose to express opinion after a certain period on public opinion evolution, which is conducive to improve the guidance effect on public opinion. The frequency of releasing opinions of public person consistently has a positive impact on the final public opinion.

公众人物的信息质量与大众接受阈值的舆论演变。
公众人物是公共事件中关注度较高的节点,他们的意见会直接影响事件的发展。然而,由于理性的原因,追随者对公众人物意见的接受程度取决于公众人物意见的信息特征和自身的理解能力。为了研究公众人物的不同观点如何引导不同的追随者,我们建立了一个舆论动态模型,为舆论管理提供理论方法。在经典有界信任模型的基础上,我们提取了信息质量变量和个体信任阈值,并将其引入到两阶段舆论演化模型的构建中。然后在模拟实验中,通过调整不同的参数,分析舆情信息质量、舆情发布时间和频率对舆情的不同影响。最后,我们增加了一个案例,将真实数据、经典模型模拟数据和改进模型模拟数据进行对比,以验证模型的有效性。研究发现,论证越充分、态度越温和,越容易引导舆论。如果公众持有不同的观点,信息质量也不同,则应选择不同的时间发表观点,以达到理想的引导效果。当公众持中立意见,信息质量相对一般时,可以尽快介入舆论,控制最终舆论;当公众持极端意见,信息质量相对较高时,可以选择在舆论演变到一定阶段后再发表意见,有利于提高舆论引导效果。公众发布舆情的频率对最终舆情具有持续的积极影响。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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