Using online negative emotions to predict risk-coping behaviors in the relocation of Beijing municipal government.

This article explores the use of online negative emotions to predict public risk-coping behaviors during urban relocation. Through a literature review, the paper proposes hypotheses that anticipate advanced prediction of public risk-coping behaviors based on online negative emotions. The study's empirical focus is on the relocation of the Beijing municipal government, using time series data for Granger causality analysis in EViews 10.0 software. Data on online negative emotions is sourced from Sina Weibo. After data cleaning, 1420 pieces of data related to the relocation policy of the Beijing Municipal Government within the period from June 9, 2015 to April 28, 2019 are retained. while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes, the data coverage is also from June 9, 2015 to April 28, 2019. The results indicated that: (1) Online negative emotions regarding the relocation policy predict public risk-coping behaviors in advance. (2) Negative comments are more effective predictors than negative feelings; (3) Negative emotions about relocation policy formulation predict risk-coping behaviors better than those related to policy effectiveness and implementation; (4) Negative emotions from individuals better predict public risk-coping behaviors than those from institutions; (5) Negative emotions from key stakeholders better predict public risk-coping behaviors than those from non-key or marginal stakeholders. It is recommended that relevant departments establish a real-time monitoring system to track negative public opinions and emotions expressed online, adopt a stakeholder-centric approach to facilitate communication, and promote transparency and educational campaigns to address the challenges of urban relocation. In future studies, methods such as expanding the sample size and adding indicators will be used to address the limitations of potential bias in sample data.