分析禽流感暴发的社交媒体话语

Marzieh Soltani , Shayan Sharif , Rozita Dara
{"title":"分析禽流感暴发的社交媒体话语","authors":"Marzieh Soltani ,&nbsp;Shayan Sharif ,&nbsp;Rozita Dara","doi":"10.1016/j.nlp.2025.100176","DOIUrl":null,"url":null,"abstract":"<div><div>The ongoing avian influenza outbreaks have had significant implications for the global poultry industry in addition to a wide range of wild birds and mammals. To enhance our understanding of public perceptions and reactions during such outbreaks, the present study examined social media discourse surrounding avian influenza on X (formerly known as Twitter). By employing advanced large language models, including DistilBERT for post filtering (average 89.5% accuracy via 5-fold cross-validation) along with Mixtral-8x7B, BERTopic, and RoBERTa for sentiment and topic/user analysis, this research categorizes the discussions and sentiments expressed by users over time. Our analysis focused on three aspects: main topics, sentiment, and temporal patterns of user engagement surrounding avian influenza outbreaks. Sentiment analysis revealed that a majority of posts related to economic impact (81.2%), wildlife (71.7%), and human cases (67.9%) expressed negative sentiment. Through topic modeling, prevalent topics of concern were identified in discussions, including concerns about transmission to humans and mammals, as well as issues related to food security and food prices. Additionally, the analysis of user engagement patterns showed distinct categories of users and highlighted the contributions of top users in shaping the discourse. Emotion analysis showed that over 80% of posts on major topics conveyed emotions such as anger, sadness, and fear, especially during periods of high case reports. The present study underscores the potential of social media analysis to understand public reactions to avian influenza outbreaks and to facilitate effective responses to public concerns and needs.</div></div>","PeriodicalId":100944,"journal":{"name":"Natural Language Processing Journal","volume":"12 ","pages":"Article 100176"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Analyzing social media discourse of avian influenza outbreaks\",\"authors\":\"Marzieh Soltani ,&nbsp;Shayan Sharif ,&nbsp;Rozita Dara\",\"doi\":\"10.1016/j.nlp.2025.100176\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The ongoing avian influenza outbreaks have had significant implications for the global poultry industry in addition to a wide range of wild birds and mammals. To enhance our understanding of public perceptions and reactions during such outbreaks, the present study examined social media discourse surrounding avian influenza on X (formerly known as Twitter). By employing advanced large language models, including DistilBERT for post filtering (average 89.5% accuracy via 5-fold cross-validation) along with Mixtral-8x7B, BERTopic, and RoBERTa for sentiment and topic/user analysis, this research categorizes the discussions and sentiments expressed by users over time. Our analysis focused on three aspects: main topics, sentiment, and temporal patterns of user engagement surrounding avian influenza outbreaks. Sentiment analysis revealed that a majority of posts related to economic impact (81.2%), wildlife (71.7%), and human cases (67.9%) expressed negative sentiment. Through topic modeling, prevalent topics of concern were identified in discussions, including concerns about transmission to humans and mammals, as well as issues related to food security and food prices. Additionally, the analysis of user engagement patterns showed distinct categories of users and highlighted the contributions of top users in shaping the discourse. Emotion analysis showed that over 80% of posts on major topics conveyed emotions such as anger, sadness, and fear, especially during periods of high case reports. The present study underscores the potential of social media analysis to understand public reactions to avian influenza outbreaks and to facilitate effective responses to public concerns and needs.</div></div>\",\"PeriodicalId\":100944,\"journal\":{\"name\":\"Natural Language Processing Journal\",\"volume\":\"12 \",\"pages\":\"Article 100176\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-08-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Natural Language Processing Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949719125000524\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Natural Language Processing Journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949719125000524","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

目前的禽流感疫情除了对各种野生鸟类和哺乳动物造成影响外,还对全球家禽业产生了重大影响。为了加强我们对此类疫情期间公众看法和反应的理解,本研究审查了X(以前称为Twitter)上围绕禽流感的社交媒体话语。通过采用先进的大型语言模型,包括用于后过滤的蒸馏器(通过5倍交叉验证的平均准确率为89.5%)以及用于情感和主题/用户分析的Mixtral-8x7B、BERTopic和RoBERTa,本研究对用户随时间表达的讨论和情感进行了分类。我们的分析集中在三个方面:围绕禽流感爆发的主要话题、情绪和用户参与的时间模式。情绪分析结果显示,经济影响(81.2%)、野生动物(71.7%)、人类事件(67.9%)相关的大部分是负面情绪。通过主题建模,确定了讨论中普遍关注的主题,包括对人类和哺乳动物传播的关注,以及与粮食安全和粮食价格有关的问题。此外,对用户参与模式的分析显示了不同类别的用户,并突出了顶级用户在塑造话语方面的贡献。情绪分析显示,在主要话题上,超过80%的帖子表达了愤怒、悲伤和恐惧等情绪,尤其是在病例报告高的时期。本研究强调了社会媒体分析在了解公众对禽流感爆发的反应和促进有效应对公众关切和需求方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analyzing social media discourse of avian influenza outbreaks

Analyzing social media discourse of avian influenza outbreaks
The ongoing avian influenza outbreaks have had significant implications for the global poultry industry in addition to a wide range of wild birds and mammals. To enhance our understanding of public perceptions and reactions during such outbreaks, the present study examined social media discourse surrounding avian influenza on X (formerly known as Twitter). By employing advanced large language models, including DistilBERT for post filtering (average 89.5% accuracy via 5-fold cross-validation) along with Mixtral-8x7B, BERTopic, and RoBERTa for sentiment and topic/user analysis, this research categorizes the discussions and sentiments expressed by users over time. Our analysis focused on three aspects: main topics, sentiment, and temporal patterns of user engagement surrounding avian influenza outbreaks. Sentiment analysis revealed that a majority of posts related to economic impact (81.2%), wildlife (71.7%), and human cases (67.9%) expressed negative sentiment. Through topic modeling, prevalent topics of concern were identified in discussions, including concerns about transmission to humans and mammals, as well as issues related to food security and food prices. Additionally, the analysis of user engagement patterns showed distinct categories of users and highlighted the contributions of top users in shaping the discourse. Emotion analysis showed that over 80% of posts on major topics conveyed emotions such as anger, sadness, and fear, especially during periods of high case reports. The present study underscores the potential of social media analysis to understand public reactions to avian influenza outbreaks and to facilitate effective responses to public concerns and needs.
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
自引率
0.00%
发文量
0
×
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:604180095
Book学术官方微信