Analysis of the Psychological Mechanisms of Online Group Polarization from a Big Data Perspective

DOI: https://doi.org/10.62517/jbdc.202601404

Author(s)

Yuxi Liu

Affiliation(s)

College of St Hild & St Bede, Durham University, Durham, United Kingdom

Abstract

Recommendation algorithms use behavioural data to customise content delivery, reduce the diversity of information, strengthen the information cocoon effect, and drive group opinions towards the extremes. Psychologically, the demand for cognitive closure is systematically stimulated; Social comparison bias and deindividuation effects combine to form opinion resonance, and emotional contagion mechanisms accelerate the spread of extreme views. A BERT-LDA fusion model is introduced in this paper to conduct structured analysis of typical public opinion events on Weibo, a three-stage polarization evolution model of "Algorithm Reinforcement–Cognitive Shift–Emotional Resonance" is built, and the psychological mechanism is quantitatively verified. At the level of governance, a three-dimensional coordinated approach of algorithm optimisation, psychological intervention and institutional coordination is needed to address the root causes of generation and spread of online group polarisation.

Keywords

Online Group Polarization; Information Cocoon; Cognitive Closure; Emotional Contagion; BERT-LDA Model

References

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