基于超网络的突发事件舆情逆转识别研究。

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2025-08-22 DOI:10.1177/2167647X251366060
Xuna Wang
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引用次数: 0

摘要

随着社交媒体和网络平台的快速发展,突发事件在网络空间的传播速度和影响力显著提高。社会舆论的急剧变化,特别是舆论倒转现象,对社会稳定和政府公信力产生了深刻的影响。以多层次、多维复杂性为特征的超网络结构为分析舆论演变过程中的多智能体及其相互作用提供了一个新的理论框架。本研究基于超网络理论,构建了包含用户交互网络、事件演化网络、语义关联网络和情感传导网络的四层子网模型。通过提取网络结构特征,进行跨层联动分析,建立突发事件舆情逆转识别体系。以2021年河南暴雨期间红星尔克捐赠事件为例,实证分析舆论逆转过程。研究结果表明,所提出的超网络模型能够有效地识别舆情逆转的关键节点。舆论逆转多指标协同识别系统有助于快速有效地发现舆论逆转信号。本研究不仅为舆情逆转动态识别提供了新的方法支持,也为突发事件中舆情监测和应急决策提供了理论参考和实践指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study of Public Opinion Reversal Recognition of Emergency Based on Hypernetwork.

With the rapid development of social media and online platforms, the speed and influence of emergency dissemination in cyberspace have significantly increased. The swift changes in public opinion, especially the phenomenon of opinion reversals, exert profound impacts on social stability and government credibility. The hypernetwork structure, characterized by its multilayered and multidimensional complexity, offers a new theoretical framework for analyzing multiagents and their interactions in the evolution of public opinion. Based on hypernetwork theory, this study constructs a four-layer subnet model encompassing user interaction network, event evolution network, semantic association network, and emotional conduction network. By extracting network structural features and conducting cross-layer linkage analysis, an identification system for public opinion reversals in emergencies is established. Taking the donation incident involving Hongxing Erke during the Henan rainstorm in 2021 as a case study, an empirical analysis of the public opinion reversal process is conducted. The research results indicate that the proposed hypernetwork model can effectively identify key nodes in public opinion reversals. The multi-indicator collaborative identification system for public opinion reversals aids in rapidly and effectively detecting signals of such reversals. This study not only provides new methodological support for the dynamic identification of public opinion reversals but also offers theoretical references and practical guidance for public opinion monitoring and emergency response decision-making in emergencies.

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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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