Public Opinion Risk Early Warning Model on Government-Citizen Interaction Data: A Perspective on Evidence-based Decision Making

[Purpose/Significance] The study aims to construct an early warning model of public opinion risks based on government-citizen interaction data, guided by evidence-based decision-making theory. We seek to uncover the governance value embedded in such interaction data, providing new insights and methods for identifying and managing potential public opinion risks. Traditional methods of monitoring public opinion often rely on subjective judgment, leading to potential bias and inefficiency. In contrast, this study uses objective, data-driven techniques to improve the accuracy and reliability of risk predictions. By integrating evidence-based decision making with public opinion analysis, the study not only advances the theoretical framework but also provides practical tools for government use. This innovation is significant as it addresses the gaps in the current literature regarding the objective assessment of public opinion risks and their impact on governance, thereby contributing to the field of public administration and social governance. [Method/Process] The research methodology involves a multi-step process, starting with the identification of key indicators of public opinion risks. These indicators include appeal purpose, text length, sensitivity, emotional tendency, and degree of aggregation. The analytical hierarchy process (AHP) and the criteria importance through intercriteria correlation (CRITIC) method were employed to calculate the weight of each indicator. AHP, a subjective weighting method, uses expert judgement to construct a judgement matrix and determine indicator weights. However, to reduce subjective bias, the CRITIC method is integrated, which objectively determines weights based on the variability and conflict in the data. The model's workflow began with problem identification, which captures the issues that government officials want to address through public opinion monitoring. Data were then collected from various channels, such as the "12345" government service hotline, government Weibo accounts, and official email inboxes. The risk identification phase involves the construction of a public opinion risk identification index system to identify potential risks in the data collected. This is followed by a risk assessment, where the weight of each indicator is calculated, and the risks are classified into different levels. Finally, decision recommendations were provided based on the risks identified and their urgency. The model was validated using government-citizen interaction data from Suzhou as a case study. The results of the analysis were closely aligned with the future priorities of the Suzhou municipal government, fully demonstrating the model's effectiveness and reliability of the model for early risk warning. [Results/Conclusions] The study concludes with the validation of a feasible and practical early warning model for public opinion risks. The model was tested using interaction data from the Suzhou municipal government's official website, demonstrating its effectiveness in identifying and predicting public opinion risks. The results show that the model can accurately assess the severity of risks and provide timely warnings, helping government decision-makers to manage risks proactively.