The measurement and influence factors of social consensus in the context of public events
Ruiyang Chen et al.
What the paper says
Introduction. This study examines the formation of social consensus during public events by introducing measurable indicators and identifying key influencing factors. Method. Social consensus was defined and measured in terms of its breadth and strength. Factors influencing the formation of social consensus was identified. The SHAP interpretation method was applied to analyse key drivers of social consensus formation, followed by a configuration analysis of core influencing factors using fsQCA to identify the configuration path to achieve consensus. Analysis. A dataset of 155,740 posts was collected from Weibo. Four machine learning regression models were trained and evaluated. The best-performing model was further interpreted using SHAP to identify factors’ contributions. FsQCA revealed three distinct pathways to high consensus and two pathways to low consensus. Results. Consensus is negatively associated with the proportion of individuals with higher education, age difference, and the proportion of individuals with firm opinions, while positively associated with social network structure similarity. Groups that achieve high consensus levels have three key characteristics. Conclusion(s). The study provides a systematic framework for quantifying and analysing social consensus in public events. The findings clarify both consistent predictors and context-specific mechanisms, offering practical insights for guiding public opinion and managing polarization during crises.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.