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“一场比赛真的是人们丧命的理由吗?” 基于推特的关于印尼足球踩踏事件的话语情感与主题分析

"Is a game really a reason for people to die?" Sentiment and thematic analysis of Twitter-based discourse on Indonesia soccer stampede.

作者信息

Ujah Otobo I, Ogbu Chukwuemeka E, Kirby Russell S

机构信息

Chiles Center, College of Public Health, University of South Florida, 33612 Tampa Florida, USA.

出版信息

AIMS Public Health. 2023 Sep 5;10(4):739-754. doi: 10.3934/publichealth.2023050. eCollection 2023.

DOI:10.3934/publichealth.2023050
PMID:38187902
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10764967/
Abstract

This study examined discourses related to an Indonesian soccer stadium stampede on 1st October 2022 using comments posted on Twitter. We conducted a lexicon-based sentiment analysis to identify the sentiments and emotions expressed in tweets and performed structural topic modeling to identify latent themes in the discourse. The majority of tweets (87.8%) expressed negative sentiments, while 8.2% and 4.0% of tweets expressed positive and neutral sentiments, respectively. The most common emotion expressed was fear (29.3%), followed by sadness and anger. Of the 19 themes identified, "Deaths and mortality" was the most prominent (15.1%), followed by "family impact". The negative stampede discourse was related to public concerns such as "vigil" and "calls for bans and suspension," while positive discourse focused more on the impact of the stampede. Public health institutions can leverage the volume and rapidity of social media to improve disaster prevention strategies.

摘要

本研究利用推特上发布的评论,对与2022年10月1日印度尼西亚足球场踩踏事件相关的话语进行了分析。我们进行了基于词汇的情感分析,以识别推文中表达的情感和情绪,并进行了结构化主题建模,以识别话语中的潜在主题。大多数推文(87.8%)表达了负面情绪,而分别有8.2%和4.0%的推文表达了正面和中性情绪。表达最常见的情绪是恐惧(29.3%),其次是悲伤和愤怒。在确定的19个主题中,“死亡与死亡率”最为突出(15.1%),其次是“家庭影响”。负面的踩踏事件话语与公众关注的“守夜”和“呼吁禁令和暂停”等问题相关,而正面话语则更多地关注踩踏事件的影响。公共卫生机构可以利用社交媒体的数量和速度来改进防灾策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73fd/10764967/3d705d43e11d/publichealth-10-04-050-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73fd/10764967/df44ae6acd97/publichealth-10-04-050-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73fd/10764967/3d705d43e11d/publichealth-10-04-050-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73fd/10764967/df44ae6acd97/publichealth-10-04-050-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/73fd/10764967/3d705d43e11d/publichealth-10-04-050-g002.jpg

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2
Text mining for social science - The state and the future of computational text analysis in sociology.文本挖掘在社会科学中的应用——社会学中计算文本分析的现状与未来。
Soc Sci Res. 2022 Nov;108:102784. doi: 10.1016/j.ssresearch.2022.102784. Epub 2022 Sep 2.
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The Crowd Crush at Mount Meron: Emergency Medical Services Response to a Silent Mass Casualty Incident.
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Disaster Med Public Health Prep. 2022 Jul 21:1-3. doi: 10.1017/dmp.2022.162.
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Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study.利用标签#scamdemic 和 #plandemic 分析推特上的 COVID-19 虚假信息:回顾性研究。
PLoS One. 2022 Jun 22;17(6):e0268409. doi: 10.1371/journal.pone.0268409. eCollection 2022.
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Tweet topics and sentiments relating to distance learning among Italian Twitter users.对意大利推特用户关于远程学习的推文主题和情绪进行分析。
Sci Rep. 2022 Jun 2;12(1):9163. doi: 10.1038/s41598-022-12915-w.
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