|
|
|
| Research on Herd Effect Identification in Online Public Opinion Events |
| Shen Wang, Li Xin, Yan Zhipeng, Li He |
| School of Business and Management, Jilin University, Changchun 130012 |
|
|
|
|
Abstract This study focuses on identifying the herding effect in online public opinion events, aiming to systematically construct a multidimensional quantitative identification method for it and further explore the dynamic evolution of this effect. First, this study analyzes and deconstructs the herding effect in online public opinion. Then, it constructs a dynamic user network that integrates semantic features and emotional intensity, and employs the Louvain-Blondel algorithm for dynamic community detection among users. Subsequently, it systematically identifies the herding effect across four dimensions: the explosiveness of dissemination, the influence of dominant communities, group aggregation, and the convergence of information content. Empirical research shows that the Herding Index follows a dynamic trajectory of “rapid rise-fluctuation-resurgence-decline,” with changes in modularity serving as a key driving factor. Specifically, in the outbreak stage of public opinion, the modularity decreases significantly, and the dominant community is formed rapidly to promote the aggregation of opinions; at the point of major information disclosure, the decline in modularity again can trigger a nonlinear resurgence of the herd effect, indicating that external stimuli have a significant activating effect on group behavior. In terms of validity verification, the herd effect index is highly synchronized with the change in the number of comments (Pearson correlation coefficient r = 0.9172, p < 0.001). Exploratory Factor Analysis (EFA) demonstrates that the four indicators had good construct validity and can effectively measure the core characteristics of the herding effect. The fitting and prediction results of the grey prediction model (GM (1,1)) show that the index has good trend response and prediction capabilities in different stages, especially showing high prediction accuracy in the stable evolution stage, which verifies the effectiveness of the identification method.
|
|
Received: 08 October 2025
|
|
|
|
1 中国互联网络信息中心. 第57次《中国互联网络发展状况统计报告》[EB/OL]. (2026-02-05) [2026-03-11]. https://cnnic.net.cn/n4/2026/0304/c88-11549.html.
2 Banerjee A V. A simple model of herd behavior[J]. The Quarterly Journal of Economics, 1992, 107(3): 797-817.
3 乌吉斯古楞, 王晰巍, 王楠阿雪. 智能推荐算法作用下重大突发事件群体信息从众行为研究——基于社会学习理论视角[J]. 图书情报工作, 2024, 68(20): 87-103.
4 Kong Y X, Wu R J, Zhang Y C, et al. Utilizing statistical physics and machine learning to discover collective behavior on temporal social networks[J]. Information Processing & Management, 2023, 60(2): 103190.
5 章瑞, 钱敏. 追随、缄默与群体极化: 突发事件网络舆情中的羊群效应[J]. 铜陵学院学报, 2020, 19(6): 65-67, 86.
6 窦云莲. 算法推送下舆情信息圈层化传播及风险治理[J]. 中国人民警察大学学报, 2024, 40(12): 35-40.
7 Asch S E. Effects of group pressure upon the modification and distortion of judgments[M]// Documents of Gestalt Psychology. Oakland: University of California Press, 1961: 222-236.
8 Del Vicario M, Bessi A, Zollo F, et al. The spreading of misinformation online[J]. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(3): 554-559.
9 马宇红, 赵媛媛, 强亚蓉. 社交网络中谣言传播的从众效应和权威效应[J]. 计算机应用, 2019, 39(1): 232-238.
10 Chen H T. Spiral of silence on social media and the moderating role of disagreement and publicness in the network: analyzing expressive and withdrawal behaviors[J]. New Media & Society, 2018, 20(10): 3917-3936.
11 彭兰. 导致信息茧房的多重因素及“破茧”路径[J]. 新闻界, 2020(1): 30-38, 73.
12 王乐, 王璐瑶, 张紫琼, 等. 社交媒体环境下泄露他人隐私意向和行为的增强与反转机制研究[J]. 中国管理科学, 2026, 34(5): 97-108.
13 莫祖英, 王垲烁, 赵悦名. 社交媒体用户虚假信息从众传播行为影响模型实证研究[J]. 情报资料工作, 2023, 44(1): 72-81.
14 Ma C L, Ren Y L, Castells P, et al. Evaluation of herd behavior caused by population-scale concept drift in collaborative filtering[C]// Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM Press, 2022: 1984-1989.
15 朱恒民, 苏新宁, 张相斌. 互联网舆情演化的动态网络模型研究[J]. 情报理论与实践, 2010, 33(10): 75-78.
16 Snijders T A B, van de Bunt G G, Steglich C E G. Introduction to stochastic actor-based models for network dynamics[J]. Social Networks, 2010, 32(1): 44-60.
17 吴江, 于洋, 丁宏昊, 等. 时变网络研究: 概念、应用与展望[J]. 情报学报, 2025, 44(1): 103-122.
18 强子珊, 顾益军. 融合动态传播和社区结构的社交媒体谣言检测模型[J]. 计算机工程与应用, 2024, 60(18): 198-207.
19 李贺, 刘嘉宇, 沈旺, 等. 社交参与视角下超图增强的学习趣缘社群群体检测研究[J]. 情报学报, 2024, 43(12): 1425-1439.
20 卢国强, 马海群. 信息茧房的形态辨析: 抽象概念与科学量化的交融探索[J]. 情报资料工作, 2025, 46(6): 15-24.
21 Turner J C, Hogg M A, Oakes P J, et al. Rediscovering the social group: a self-categorization theory[J]. Contemporary Sociology, 1987, 94(6): 645-646.
22 Ouyang F, Ling T J, Jiao P C. Development of group cognition in online collaborative problem-solving processes[J]. Journal of Educational Computing Research, 2022, 60(3): 599-630.
23 Ahajjam S, Badir H. Identification of influential spreaders in complex networks using HybridRank algorithm[J]. Scientific Reports, 2018, 8(1): article No.11932.
24 岳芳, 戴文慧, 潘崇林, 等. 人机协同视角下的观点演化模型及其在“回音室效应”研究中的应用[J]. 情报理论与实践, 2025, 48(8): 113-123.
25 Sun H S. A longitudinal study of herd behavior in the adoption and continued use of technology[J]. MIS Quarterly, 2013, 37(4): 1013-1041.
26 Wang T, Wang D S, Wang F. Quantifying herding effects in crowd wisdom[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2014: 1087-1096.
27 苏剑林. CoSENT(一): 比Sentence-BERT更有效的句向量方案[EB/OL]. (2022-01-06) [2025-06-11]. https://kexue.fm/archives/8847.
28 Zhou Y Y, Hu Y R, Liu H J, et al. Linking negative herding effect to online public opinion: a new BiGADG approach for sentiment classification[J]. Expert Systems, 2024, 41(8): e13587.
29 安璐, 郑雅静. 突发事件情境下社会共识形成机理[J]. 数据分析与知识发现, 2024, 8(12): 1-17.
30 Zhao G S, Lou P L, Qian X M, et al. Personalized location recommendation by fusing sentimental and spatial context[J]. Knowledge-Based Systems, 2020, 196: 105849.
31 Wang G H, Chi Y X, Liu Y J, et al. Studies on a multidimensional public opinion network model and its topic detection algorithm[J]. Information Processing & Management, 2019, 56(3): 584-608.
32 Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008.
33 王旭娜, 张立凡. 基于超网络的重大突发事件公众认知演化与预警策略研究[J]. 情报理论与实践, 2024, 47(12): 183-194.
34 程启月. 评测指标权重确定的结构熵权法[J]. 系统工程理论与实践, 2010, 30(7): 1225-1228.
35 Abbas J, Batool I, Batool H, et al. Development and validation of a digital conformity scale: what should be the measurement criteria with social media exposure?[J]. Kybernetes, 2026. DOI: 10.1108/K-05-2024-1393.
36 Kayacan E, Ulutas B, Kaynak O. Grey system theory-based models in time series prediction[J]. Expert Systems with Applications, 2010, 37(2): 1784-1789. |
|
|
|
