基于情绪分析的事故风险感知偏差研究

Study on accident risk perception bias based on emotion analysis

为探索公众对安全事故的风险感知,运用文本挖掘技术获得事故微博评论数据,采用中文风险感知双向编码转换器(BERT-RPC)识别惊讶和恐惧情绪,以频率惊讶测量事故概率感知偏差,以恐惧和损失惊讶测量事故损失感知偏差,基于二元逻辑回归考察微博形式和内容对风险感知的影响。结果表明:低估安全事故风险的现象普遍存在,且对事故损失的低估更为突出;基于BERT-RPC模型的“抓取-分析”技术能够高效、低延迟地实现全网公众的风险感知偏差监测;交通行业的事故概率、损失被严重低估;一般事故的概率和特大事故的损失被严重低估;事故图片和视频有助于纠正事故损失的感知偏差,但对概率感知偏差作用有限;事故爆发初期报道对公众风险感知纠正效果最佳,调查结果公布和责任人宣判阶段次之。

In order to explore the public's risk perception of safety accidents,text mining technology was used to obtain accident micro-blog comment data,emotions like surprise and fear were identified by adopting BERT-RPC.Then,accident probability perception bias was measured by frequency surprise,and accident loss perception bias was measured by fear and loss surprise.Finally,influence of micro-blog form and content on risk perception was investigated by binary logic regression.The results show that the underestimation of safety accident risks is very common,and it is more serious in loss perception.The"capture-analysis"technology based on BERT-RPC model can monitor risk perception bias of the public in the whole network with high efficiency and low delay.Moreover,accident probability and loss in transportation industry are significantly underestimated,and so are the probability of general accidents and loss of major ones.While accident pictures and videos can help correct perception bias of accident loss,their effect on probability perception is limited.Accident reports in the early stage has the best correction effect on risk perception,and then in stages where survey results and people held accountable are announced.