重大突发公共卫生事件下的公众情感演进分析:基于新冠肺炎疫情的考察  

Evolution of Public Sentiments During COVID-19 Pandemic

作　　者：边晓慧[1] 徐童[2] Bian Xiaohui;Xu Tong(School of Management,Anhui University,Hefei 230039,China;School of Computer Science,University of Science and Technology of China,Hefei 230027,China)

机构地区：[1]安徽大学管理学院,合肥230039 [2]中国科学技术大学计算机科学与技术学院,合肥230027

出　　处：《数据分析与知识发现》2022年第7期128-140,共13页Data Analysis and Knowledge Discovery

基　　金：国家社会科学基金项目(项目编号:16CZZ025)的研究成果之一。

摘　　要：【目的】对重大突发公共卫生事件背景下公众在社交媒体中的情感表达进行分析,揭示疫情期间公众情感的时空差异、不同主题下的情感演化以及情感的跨地域扩散。【方法】利用主题模型提炼潜在话题与关键词群,从全局视角和主题视角探究公众情感演进趋势,并使用社交传播模型描述公众情感的跨地域扩散。【结果】疫情期间公众以积极情感为主,消极情感呈现“恶”的情感主导、“惧”的情感先发、“哀”的情感反复等特征;疫区距离与经济水平导致公众情感存在空间差异。同时,情感表达及演化趋势因受时空变化、主题/事件区别等影响而具有一定规律的差异。此外,公众情感的地域扩散强度受空间关系和疫情严重性的双重影响。【局限】面向纯文本信息,无法对多模态信息如视频、图片等进行综合性分析。【结论】重大突发公共卫生事件下,公众在社交媒体上的情感表达及演化趋势受时空差异、主题差异等影响,并存在一定的地域扩散规律。这提示疫情防控要结合特定时期、特定地域采取差异化策略,关注不同主题类型对情感的关联性影响,同时注重疫情防控与舆情监控的区域统筹与合作,以实现对舆情的积极引导和公众情感的有效疏解。[Objective] This study analyzes the social media posts during the COVID-19 pandemic, aiming to reveal the temporal and spatial differences of public opinion, the sentiment evolution under different circumstances, as well as the trans-regional spreading of the public sentiments. [Methods] Firstly, we utilized the Latent Dirichlet Allocation(LDA) model to generate the latent topics and related keyword groups, which also analyzed public sentiment evolutions from the perspectives of global and individual topics. Then, we described the trans-regional spread of public sentiments based on the social spread model adapted from the classic Independent Cascade Model. [Results] The new model summarized the general rules of the temporal evolution and spatial difference, as well as the impacts of distance to the epidemic centers and the financial levels. We also found two different types of topics indicating reasons for popularity and sentiment differences, as well as multi-view connections among these topics. The strength of trans-regional sentiment spread could be affected by both regional distance and epidemic situation. [Limitations] The new framework could not process the multimodal data. [Conclusions] The proposed model helps the local government make better strategies according to specific conditions, and pay more attention to the impacts of related events. They should also strengthen regional cooperation and coordination for controlling pandemics and monitoring public sentiments.

关 键 词：突发公共卫生事件 微博舆情 情绪演化 主题分析 时空分析 

分 类 号：G203[文化科学—传播学]