INFORMATION RESEARCH

Research on Group Information Herd Behavior in Major Emergent Events Under the Influence of Intelligent Recommendation Algorithms: A Social Learning Theory Perspective

  • Wuji Siguleng ,
  • Wang Xiwei ,
  • Wang Nanaxue
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  • 1 School of Business and Management, Jilin University, Changchun 130022;
    2 School of Economics and Management, Inner Mongolia University, Hohhot 010021;
    3 Institute of National Development and Security Studies, Jilin University, Changchun 130022;
    4 Research Center for Big Data Management, Jilin University, Changchun 130022

Received date: 2024-03-26

  Revised date: 2024-06-21

  Online published: 2024-10-29

Supported by

This work is supported by the general project of the Jilin Natural Science Foundation titled “Research on the Impact of Intelligent Recommendation Algorithms on the Evolution of Online Public Opinion and Risk Warning in Major Emergencies” (Grant No. 20240101372JC).

Abstract

[Purpose/Significance] In the current context of frequent major emergencies, the pivotal role of social media has become evident. Although its intelligent recommendation algorithms have increased the visibility of disaster information, they might also exacerbate the spread of internet rumors and the fermentation of group information herd behavior. Understanding the influencing factors of group information behavior in major emergencies under the intelligent recommendation algorithms can reveal the tendencies and decision-making mechanisms of group information following, enabling the formulation of more effective crisis management and emergency plans. [Method/Process] This paper combined content coding analysis with fuzzy-set Qualitative Comparative Analysis (fsQCA) methods, adopting an exploratory-then-confirmatory followed by a verification research strategy, and designed a sequential mixed research method. It identified the key factors influencing group information herd behavior in the context of major emergencies based on social learning theory. The fsQCA method was used to construct a configurational path analysis framework. [Result/Conclusion] The study identifies eight key factors influencing group information herd behavior during major emergencies, including observational learning, perceived social influence, recommendation algorithmic affordance, recommendation algorithm experience, perceived social value, perceived social identity, self-regulation, and self-efficacy. Based on these factors, it discovers three distinct types of information herd behavior in two categories: information herd behavior driven by herd mechanism factors, information herd behavior involved by triadic interactions and imitation of others, information herd behavior involved by triadic interactions and discount own information, and non-herd information behavior. These findings offer new perspectives for a deeper understanding of the role of social media in responding to major emergencies and provide theoretical support for optimizing intelligent recommendation algorithms on social media platforms in specific scenarios.

Cite this article

Wuji Siguleng , Wang Xiwei , Wang Nanaxue . Research on Group Information Herd Behavior in Major Emergent Events Under the Influence of Intelligent Recommendation Algorithms: A Social Learning Theory Perspective[J]. Library and Information Service, 2024 , 68(20) : 87 -103 . DOI: 10.13266/j.issn.0252-3116.2024.20.008

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