Abstract:

[Purpose/Significance] In the era of widespread social media, network cluster behavior has emerged as a significant phenomenon that shapes online public opinion and collective action. Although existing research has thoroughly examined macro-level drivers and developed evolutionary stage models for network cluster behavior, there is still a significant gap in our understanding of the micro-level cognitive mechanisms that dynamically propel its evolution. Cognitive biases, which are inherent tendencies in human cognition, are amplified in online group interactions. This study specifically addresses this gap by adopting a cognitive bias perspective to investigate the evolution mechanism of network cluster behavior. It is crucial to focus on campus hot events as highly relevant and sensitive case studies. These events often involve students, parents, educational institutions, and the wider public, covering core issues such as campus safety, management disputes, teacher-student relations, and student rights. Their inherent emotional resonance, rapid dissemination within specific online communities, and potential for severe damage to reputation and social order necessitate deeper understanding. The core innovation and significance of this research lie in: 1) Systematically integrating cognitive bias theory to analyze the complete lifecycle evolution of network cluster behavior in campus events; 2) Empirically revealing how specific biases dynamically manifest and interact at various stages, shaping the trajectory of network cluster behavior; 3) Providing a richer theoretical framework for network cluster action theory; 4) Offering empirical evidence for formulating targeted governance strategies to mitigate risks associated with campus-related online crises, thereby promoting constructive online discourse and campus stability. [Method/Process] To rigorously investigate the core research question, this study employed the grounded theory methodology. Based on sustained high popularity rankings on the "Zhiwei Shijian" platform, ten representative campus hot events were systematically selected to ensure coverage of diverse campus issues. Extensive datasets of user comments related to these ten events were collected from the Sina Weibo platform, serving as the core empirical foundation. The data collection timeframe spanned the complete lifecycle of each event, from initial emergence to eventual subsidence. Following the grounded theory process, the collected textual data underwent a meticulous three-stage coding procedure to induce and refine textual themes. Through this process, facilitated by qualitative data analysis software, a substantive theoretical model was ultimately constructed. This model delineates the evolutionary path and internal mechanisms of network cluster behavior in campus events under the influence of cognitive biases. The grounded theory method was deemed highly appropriate due to its capacity for deeply exploring complex social processes and emergent phenomena directly from rich, context-specific data. [Results/Conclusions] The study found that the evolution mechanism of network cluster behavior in the context of campus hot topics mainly consists of five stages: public opinion induction, public opinion bias, public opinion diffusion, public opinion outbreak, and public opinion subsidence. Based on these findings, governance strategies for such campus network events have been proposed, including identifying triggering factors, avoiding cognitive biases, enhancing user literacy, promoting collaborative guidance, and mitigating secondary risks.

Key words: online public opinion, collective behavior, campus incidents, cognitive bias, grounded theory, affective computing