The measurement and influence factors of social consensus in the context of public events
DOI:
https://doi.org/10.47989/ir31iConf64114Keywords:
Social consensus, Public opinion, Online users, Social media, Public eventAbstract
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.
References
An, L., Zhou, W., Ou, M., Li, G., & Wang, X. (2021). Measuring and profiling the topical influence and sentiment contagion of public event stakeholders. International Journal of Information Management, 58(7), 102327.
Asch, S. E. (1951). Effects of group pressure upon the modification and distortion of judgments. In Groups, leadership and men; research in human relations. (pp. 177-190). Carnegie Press.
Babaei, M., Kulshrestha, J., Chakraborty, A., Benevenuto, F., Gummadi, K. P., & Weller, A. (2018). Purple Feed: Identifying High Consensus News Posts on Social Media Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, New Orleans, LA, USA. https://doi.org/10.1145/3278721.3278761
Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130-1132. https://doi.org/10.1126/science.aaa1160
Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber? Psychol Sci, 26(10), 1531-1542. https://doi.org/10.1177/0956797615594620
Bjornsdottir, R. T., Hehman, E., & Human, L. J. (2021). Consensus Enables Accurate Social Judgments. Social Psychological and Personality Science, 13(6), 1010-1021. https://doi.org/10.1177/19485506211047095
Capurro, R. (1990). Towards an information ecology. In I. Wormell (Ed.), Information Quality: Definitions and Dimensions (pp. 122-139). Taylor Graham.
Chen, X., Zhang, W., Xu, X., & Cao, W. (2022). Managing Group Confidence and Consensus in Intuitionistic Fuzzy Large Group Decision-Making Based on Social Media Data Mining. Group Decision and Negotiation, 31(5), 995-1023. https://doi.org/10.1007/s10726-022-09787-w
Dong, Q., Sheng, Q., Martínez, L., & Zhang, Z. (2022). An adaptive group decision making framework: Individual and local world opinion based opinion dynamics. INFORMATION FUSION, 78, 218-231. https://doi.org/10.1016/j.inffus.2021.09.013
Dong, Y., Ding, Z., Martínez, L., & Herrera, F. (2017). Managing consensus based on leadership in opinion dynamics. INFORMATION SCIENCES, 397-398, 187-205. https://doi.org/10.1016/j.ins.2017.02.052
Elzinga, C., Wang, H., Lin, Z., & Kumar, Y. (2011). Concordance and consensus. INFORMATION SCIENCES, 181(12), 2529-2549. https://doi.org/10.1016/j.ins.2011.02.001
Granovetter, M. S. (1977). The Strength of Weak Ties11This paper originated in discussions with Harrison White, to whom I am indebted for many suggestions and ideas. Earlier drafts were read by Ivan Chase, James Davis, William Michelson, Nancy Lee, Peter Rossi, Charles Tilly, and an anonymous referee; their criticisms resulted in significant improvements. In S. Leinhardt (Ed.), Social Networks (pp. 347-367). Academic Press. https://doi.org/10.1016/B978-0-12-442450-0.50025-0
Greckhamer, T., Furnari, S., Fiss, P. C., & Aguilera, R. V. (2018). Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization, 16(4), 482-495. https://doi.org/10.1177/1476127018786487
Habermas, J., & Mccarthy, T. (1981). The Theory of Communicative Action.
Haneczok, J., & Piskorski, J. (2020). Shallow and deep learning for event relatedness classification. Information Processing & Management, 57(6), Article 102371. https://doi.org/10.1016/j.ipm.2020.102371
Huddy, L., & Khatib, N. (2007). American Patriotism, National Identity, and Political Involvement. American Journal of Political Science, 51(1), 63-77. http://www.jstor.org/stable/4122906
Ji, J., Zhu, Y., & Chao, N. (2023). A comparison of misinformation feature effectiveness across issues and time on Chinese social media. Information Processing & Management, 60(2), 103210. https://doi.org/10.1016/j.ipm.2022.103210
Jian, F., & Zhang, P. (2025). A comparative analysis of China-themed books in three ASEAN countries: Implications for resource development and intercultural communication. Data and Information Management, 9(2), 100081. https://doi.org/10.1016/j.dim.2024.100081
Levine, D., & Gazit, T. (2025). Unorthodox Information Sources of Coping With the COVID-19 Crisis in the Ultra-Orthodox Society. Social Science Computer Review, 43(1). https://doi.org/10.1177/08944393241246282
Lewellyn, K. B., & Muller-Kahle, M. I. (2022). A Configurational Exploration of How Female and Male CEOs Influence Their Compensation. Journal of Management, 48(7), 2031-2074, Article 01492063211027225. https://doi.org/10.1177/01492063211027225
Liang, H., Yuan, F., Zhou, Z., & Su, H. (2021). Opinion separation in leader–follower coopetitive social networks. Neurocomputing, 434, 90-97. https://doi.org/10.1016/j.neucom.2020.12.079
Liu, X. F., & Tse, C. K. (2014). Impact of degree mixing pattern on consensus formation in social networks. Physica A: Statistical Mechanics and its Applications, 407, 1-6. https://doi.org/10.1016/j.physa.2014.03.086
Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017),
Na, E.-Y., & Duckitt, J. (2003). Value Consensus and Diversity between Generations and Genders. Social Indicators Research, 62(1), 411-435. https://doi.org/10.1023/A:1022665705561
Nardi, B. A., & O'Day, V. (1999). Information Ecologies: Using Technology with Heart. The MIT Press. https://doi.org/10.7551/mitpress/3767.001.0001
Naroll, R. (1983). The moral order : an introduction to the human situation / Raoul Naroll.
O'Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673-690. https://doi.org/10.1007/s11135-006-9018-6
Park, Y.-E. (2021). Developing a COVID-19 Crisis Management Strategy Using News Media and Social Media in Big Data Analytics. Social Science Computer Review, 40, 1358 - 1375. https://doi.org/10.1177/08944393211007314
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 31 (NIPS 2018),
Ragin, C. C. (2001). Fuzzy-Set Social Science.
Rawls, J. (1987). The Idea of an Overlapping Consensus. Oxford Journal of Legal Studies, 7(1), 1-25. http://www.jstor.org/stable/764257
Rihoux, B., & Ragin, C. (2009). Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques. SAGE Publications, Inc. https://doi.org/10.4135/9781452226569
Roos, C. A., Koudenburg, N., & Postmes, T. (2020). Online Social Regulation: When Everyday Diplomatic Skills for Harmonious Disagreement Break Down. Journal of Computer-Mediated Communication, 25(6), 382-401. https://doi.org/10.1093/jcmc/zmaa011
Roth, R., Ajithkumar, P., Natarajan, G., Achuthan, K., Moon, P., Zinzow, H., & Madathil, K. C. (2021). A study of adolescents’ and young adults’ TikTok challenge participation in South India. Human Factors in Healthcare, 1, 100005. https://doi.org/https://doi.org/10.1016/j.hfh.2022.100005
Rothstein, A., & Butler, C. T. (1987). On Conflict and Consensus: A Handbook on Formal Consensus Decision Making. Food Not Bombs.
Sagi, O., & Rokach, L. (2021). Approximating XGBoost with an interpretable decision tree. INFORMATION SCIENCES, 572, 522-542. https://doi.org/10.1016/j.ins.2021.05.055
Schneider, C. Q., & Wagemann, C. (2012). Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge University Press. https://doi.org/DOI: 10.1017/CBO9781139004244
Schwartz, S. H., & Sagie, G. (2000). Value Consensus and Importance. Journal of Cross-Cultural Psychology, 31(4), 465-497. https://doi.org/10.1177/0022022100031004003
Stevens, S. S. (1966). A metric for the social consensus. Science, 151 3710, 530-541.
Sunstein, C. R. (2002). The Law of Group Polarization. Journal of Political Philosophy, 10(2), 175-195. https://doi.org/10.1111/1467-9760.00148
Tajfel, H., Billig, M. G., Bundy, R. P., & Flament, C. (1971). Social categorization and intergroup behaviour. European Journal of Social Psychology, 1(2), 149-178. https://doi.org/10.1002/ejsp.2420010202
Tran, T. N. T., Felfernig, A., & Le, V. M. (2024). An overview of consensus models for group decision-making and group recommender systems. User Modeling and User-Adapted Interaction, 34(3), 489-547. https://doi.org/10.1007/s11257-023-09380-z
Wang, D., Kong, X., Nie, X., Shang, Y., Xu, S., He, Y., Maguire, P., & Hu, Y. (2021). The effects of emotion and social consensus on moral decision-making. Ethics & Behavior, 31(8), 575-588. https://doi.org/10.1080/10508422.2020.1830404
Wu, T. (2024). Heterogeneous Opinion Dynamics Considering Consensus Evolution in Social Network Group Decision-Making. Group Decision and Negotiation, 33(1), 159-194. https://doi.org/10.1007/s10726-023-09858-6
Zhang, M., & Du, Y. (2019). Qualitative Comparative Analysis (QCA) in Management and Organization Research: Position, Tactics, and Directions. Chinese Journal of Management, 16(09), 1312-1323.
Zhang, Y., Chen, X., Gao, L., Dong, Y., & Pedryczc, W. (2022). Consensus reaching with trust evolution in social network group decision making. Expert Systems with Applications, 188, 116022. https://doi.org/10.1016/j.eswa.2021.116022
Zhou, Q., Wu, Z., Altalhi, A. H., & Herrera, F. (2020). A two-step communication opinion dynamics model with self-persistence and influence index for social networks based on the DeGroot model. INFORMATION SCIENCES, 519, 363-381. https://doi.org/10.1016/j.ins.2020.01.052
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Ruiyang Chen, Lu An, Yajing Zheng, Gang Li, Chuanming Yu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
