Predicting Risk-Coping Behaviors In the Urban Relocation Process by Online Negative Emotions：A Case from Beijing

Abstract

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, utilizing Granger causality analysis on time series data. Data on online negative emotions is sourced from Sina Weibo, while risk-coping behaviors are measured through public information search behaviors and the incidence of violent crimes. 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. This study is expected to offer valuable insights and recommendations for institutions involved in policy formulation, implementation, and evaluation.