Xu Mengqiong, Wang Juanle, Qu Zheng, Min Xiaodong, Sun Yamin
School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang, 222005, China.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Heliyon. 2024 Aug 24;10(17):e36862. doi: 10.1016/j.heliyon.2024.e36862. eCollection 2024 Sep 15.
Massive amounts of data from social media possess the potential to rapidly identify the primary issues of concern in emergency disaster management. In summer 2023, Super Typhoon Doksuri which was an exceptionally special typhoon disaster that caused severe damage to China's coastal areas and disastrous impacts in inland regions, particularly triggered the most severe rainstorm in Beijing area in over a century. To enhance typhoon hazard reduction in both coastal and interior locations, it is crucial to examine public response to these events. This study uses microblog text data from July 27 to August 3 of 2023 to map the public response to Typhoon Doksuri. The Support Vector Machine (SVM) algorithm was used to classify the microblog text in combination with the typhoon path to analyze the spatial and temporal variations of the emotions of the affected individuals. The relationship between changes in public opinion, the distribution of topics, and the major disasters triggered by the residual circulation of Typhoon Doksuri in the Beijing-Tianjin-Hebei region is discussed. The Mentougou mega-storm in Beijing area that occurred in July 2023 is a typical case. The findings demonstrate that during the typhoon event, the focus of public attention changed with the movement of the typhoon path, and various public opinion topics exhibited temporal synchronization. Public sentiment indicates that the overall supportive sentiment is higher than is fearful sentiment. Based on this, it is crucial to strengthen the Beijing-Tianjin-Hebei cooperative emergency response, and response measures were proposed related to urban flood control and drainage construction, public awareness, backward areas, secondary disasters, resident relocation, and social media technology.
社交媒体中的海量数据有潜力快速识别应急灾害管理中人们主要关注的问题。2023年夏季,超强台风“杜苏芮”是一场极其特殊的台风灾害,给中国沿海地区造成严重破坏,并在内陆地区产生灾难性影响,尤其引发了北京地区一个多世纪以来最严重的暴雨。为加强沿海和内陆地区的台风减灾工作,研究公众对这些事件的反应至关重要。本研究使用2023年7月27日至8月3日的微博文本数据,来描绘公众对台风“杜苏芮”的反应。支持向量机(SVM)算法被用于结合台风路径对微博文本进行分类,以分析受影响人群情绪的时空变化。探讨了京津冀地区公众舆论变化、话题分布与台风“杜苏芮”残余环流引发的重大灾害之间的关系。2023年7月发生在北京地区的门头沟特大暴雨就是一个典型案例。研究结果表明,在台风事件期间,公众关注的焦点随着台风路径的移动而变化,各种舆论话题呈现出时间上的同步性。公众情绪表明,总体支持情绪高于恐惧情绪。基于此,加强京津冀协同应急响应至关重要,并提出了与城市防洪排水建设、公众意识、落后地区、次生灾害、居民搬迁和社交媒体技术相关的应对措施。