基于社交媒体数据的地震舆情演变与时空分析

IF 1.2 4区 地球科学 Q3 Earth and Planetary Sciences
Chenyu Wang , Yanjun Ye , Yingqiao Qiu , Chen Li , Meiqing Du
{"title":"基于社交媒体数据的地震舆情演变与时空分析","authors":"Chenyu Wang ,&nbsp;Yanjun Ye ,&nbsp;Yingqiao Qiu ,&nbsp;Chen Li ,&nbsp;Meiqing Du","doi":"10.1016/j.eqs.2024.06.002","DOIUrl":null,"url":null,"abstract":"<div><p>As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo <em>M</em>7.4 earthquake in Qinghai Province and the Yangbi <em>M</em>6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.</p></div>","PeriodicalId":46333,"journal":{"name":"Earthquake Science","volume":"37 5","pages":"Pages 387-406"},"PeriodicalIF":1.2000,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1674451924000624/pdfft?md5=586a60d4590cfce74bfcfe257d2fe5aa&pid=1-s2.0-S1674451924000624-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Evolution and spatiotemporal analysis of earthquake public opinion based on social media data\",\"authors\":\"Chenyu Wang ,&nbsp;Yanjun Ye ,&nbsp;Yingqiao Qiu ,&nbsp;Chen Li ,&nbsp;Meiqing Du\",\"doi\":\"10.1016/j.eqs.2024.06.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo <em>M</em>7.4 earthquake in Qinghai Province and the Yangbi <em>M</em>6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.</p></div>\",\"PeriodicalId\":46333,\"journal\":{\"name\":\"Earthquake Science\",\"volume\":\"37 5\",\"pages\":\"Pages 387-406\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2024-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1674451924000624/pdfft?md5=586a60d4590cfce74bfcfe257d2fe5aa&pid=1-s2.0-S1674451924000624-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earthquake Science\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1674451924000624\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Earth and Planetary Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earthquake Science","FirstCategoryId":"89","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674451924000624","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
引用次数: 0

摘要

作为网络舆情传播的重要渠道,社交媒体平台为地震等重大灾害期间的应急管理提供了及时有效的手段。本研究重点分析了青海玛多 M7.4 级地震和云南漾濞 M6.4 级地震发生后的网络舆情。通过收集、清理和整理震后的新浪微博(简称微博)数据,我们采用潜在德里希特分配(LDA)模型提取了与这些地震相关的舆情信息。该分析包括比较与这两个事件相关的网络舆情的性质和时间演变。情绪分析利用情绪字典对震后微博中的情绪内容进行了分类,从而有助于比较研究地震后网上公众情绪的特点和时间趋势。研究结果利用地理信息系统(GIS)技术实现了可视化。分析显示,两次地震后的网络舆情存在某些共性。值得注意的是,网络参与的高峰期出现在地震后的 24 小时内,之后的 24 至 48 小时内迅速下降。网络舆论流行度的变化与余震的发生有关。经人口因素调整后,震中周边地区和四川省的网络参与度明显较高。公众情绪最初以 "恐惧 "和 "惊讶 "为主,但随着救援行动的展开,公众情绪逐渐转向积极。然而,公众在网上对每次地震的反应也有所不同。漾濞地震发生后,云南省的微博发布量居全国之首;相比之下,青海省在玛多地震发生后排名第三,这是因为青海省人口较少,且通信基础设施受损严重。这项研究为分析与地震相关的网络舆情提供了一种方法论,为加强灾后应急管理和公众心理健康支持提供了启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Earthquake Science
Earthquake Science GEOCHEMISTRY & GEOPHYSICS-
CiteScore
1.10
自引率
8.30%
发文量
42
审稿时长
3 months
期刊介绍: Earthquake Science (EQS) aims to publish high-quality, original, peer-reviewed articles on earthquake-related research subjects. It is an English international journal sponsored by the Seismological Society of China and the Institute of Geophysics, China Earthquake Administration. The topics include, but not limited to, the following ● Seismic sources of all kinds. ● Earth structure at all scales. ● Seismotectonics. ● New methods and theoretical seismology. ● Strong ground motion. ● Seismic phenomena of all kinds. ● Seismic hazards, earthquake forecasting and prediction. ● Seismic instrumentation. ● Significant recent or past seismic events. ● Documentation of recent seismic events or important observations. ● Descriptions of field deployments, new methods, and available software tools. The types of manuscripts include the following. There is no length requirement, except for the Short Notes. 【Articles】 Original contributions that have not been published elsewhere. 【Short Notes】 Short papers of recent events or topics that warrant rapid peer reviews and publications. Limited to 4 publication pages. 【Rapid Communications】 Significant contributions that warrant rapid peer reviews and publications. 【Review Articles】Review articles are by invitation only. Please contact the editorial office and editors for possible proposals. 【Toolboxes】 Descriptions of novel numerical methods and associated computer codes. 【Data Products】 Documentation of datasets of various kinds that are interested to the community and available for open access (field data, processed data, synthetic data, or models). 【Opinions】Views on important topics and future directions in earthquake science. 【Comments and Replies】Commentaries on a recently published EQS paper is welcome. The authors of the paper commented will be invited to reply. Both the Comment and the Reply are subject to peer review.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信