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信息生态视角下森林火灾网络舆情风险预警研究 被引量:1

Research on Risk Warning of Network Public Opinion for Forest Fires from the Perspective for Information Ecology
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摘要 针对不确定信息下森林火灾网络舆情风险预警问题,构建基于证据推理的直觉模糊风险预警评估模型.首先,从信息生态系统的角度,识别出森林火灾事件网络舆情风险指标体系;其次,针对指标信息的不确定性和不完整性,基于模糊规则通过直觉模糊数统一描述多种指标信息;然后,利用直觉模糊熵和模糊层次分析法联合确定各级指标权重,并采用证据推理理论完成指标信息集结融合;最后,通过直觉模糊测距确定网络舆情风险预警等级,并通过效用值法确定网络舆情风险排序.通过3个现实案例分析和验证所建模型的实用性和有效性,为森林火灾网络舆情风险评估研究提供新思路和新方法. To address the risk warning of network public opinion for forest fires under uncertain information,a risk warning evaluation model based on evidential reasoning and intuitionistic fuzziness was established.Firstly,a network public opinion risk evaluation index system of forest fires was identified based on the perspective of the information ecosystem.Secondly,given the uncertainty and incompleteness of the index information in the evaluation process,the number of intuitionistic fuzziness was used to describe multiple index information by fuzzy rules.Then,the weight of the index system was calculated via intuitionistic fuzzy entropy and fuzzy analytic hierarchy process,and evidential reasoning theory was used to complete the integration of index information.Finally,the network public opinion risk level was determined by measuring the distances of intuitionistic fuzziness,and the ranking of network public opinion risks was determined by the utility value method.The practicability and effectiveness of the model were validated through three practical cases,providing new ideas and methods for the risk warning assessment of network public opinion for forest fires.
作者 朱国庆 陈燕 郎坤 王书田 ZHU Guo-qing;CHEN Yan;LANG Kun;WANG Shu-tian(School of Shipping Economics and Management,Dalian Maritime University,Dalian 116026,China)
出处 《东北大学学报(自然科学版)》 EI CAS CSCD 北大核心 2023年第8期1208-1216,共9页 Journal of Northeastern University(Natural Science)
基金 国家重点研发计划项目(2018YFC0309600) 中央高校基本科研业务费专项资金资助项目(3132019353).
关键词 森林火灾 信息生态 网络舆情风险评估 不确定性 证据推理 forest fire information ecology network public opinion risk assessment uncertainty evidential reasoning
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