社交情报挖掘:从 X 中挖掘洞察力

IF 4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine learning and knowledge extraction Pub Date : 2023-12-11 DOI:10.3390/make5040093
Hossein Hassani, N. Komendantova, Elena Rovenskaya, M. R. Yeganegi
{"title":"社交情报挖掘:从 X 中挖掘洞察力","authors":"Hossein Hassani, N. Komendantova, Elena Rovenskaya, M. R. Yeganegi","doi":"10.3390/make5040093","DOIUrl":null,"url":null,"abstract":"Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike.","PeriodicalId":93033,"journal":{"name":"Machine learning and knowledge extraction","volume":"10 4","pages":""},"PeriodicalIF":4.0000,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Social Intelligence Mining: Unlocking Insights from X\",\"authors\":\"Hossein Hassani, N. Komendantova, Elena Rovenskaya, M. R. Yeganegi\",\"doi\":\"10.3390/make5040093\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike.\",\"PeriodicalId\":93033,\"journal\":{\"name\":\"Machine learning and knowledge extraction\",\"volume\":\"10 4\",\"pages\":\"\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Machine learning and knowledge extraction\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/make5040093\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Machine learning and knowledge extraction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/make5040093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

社会趋势挖掘处于数据科学和社会研究的交汇点,为研究社会动态和新兴趋势提供了一个新的视角。本文探讨了社会趋势挖掘的复杂面貌,特别强调了对领先趋势和滞后趋势的辨别。在此背景下,我们的研究采用了社会趋势挖掘技术,仔细研究了与风险管理、地震和灾难相关的 X(原 Twitter)数据。全面了解个人如何看待灾害风险管理中这些关键方面的重要性,对于制定获得公众认可的政策至关重要。本文揭示了公众情绪的复杂性,为政策制定者和研究人员提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Social Intelligence Mining: Unlocking Insights from X
Social trend mining, situated at the confluence of data science and social research, provides a novel lens through which to examine societal dynamics and emerging trends. This paper explores the intricate landscape of social trend mining, with a specific emphasis on discerning leading and lagging trends. Within this context, our study employs social trend mining techniques to scrutinize X (formerly Twitter) data pertaining to risk management, earthquakes, and disasters. A comprehensive comprehension of how individuals perceive the significance of these pivotal facets within disaster risk management is essential for shaping policies that garner public acceptance. This paper sheds light on the intricacies of public sentiment and provides valuable insights for policymakers and researchers alike.
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
CiteScore
6.30
自引率
0.00%
发文量
0
审稿时长
7 weeks
期刊最新文献
Knowledge Graph Extraction of Business Interactions from News Text for Business Networking Analysis Machine Learning for an Enhanced Credit Risk Analysis: A Comparative Study of Loan Approval Prediction Models Integrating Mental Health Data A Data Mining Approach for Health Transport Demand Predicting Wind Comfort in an Urban Area: A Comparison of a Regression- with a Classification-CNN for General Wind Rose Statistics An Evaluative Baseline for Sentence-Level Semantic Division
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1