利用社交媒体分析了解退伍军人的愤怒表达

Nadiyah Johnson, Joseph Coelho, Md Fitrat Hossain, Thomas Kissane, Wylie Frydrychowi, Madiraju Praveen, Zeno Franco, Katinka Hooyer, Priyanka Annapureddy, Sheikh Iqbal Ahamed
{"title":"利用社交媒体分析了解退伍军人的愤怒表达","authors":"Nadiyah Johnson, Joseph Coelho, Md Fitrat Hossain, Thomas Kissane, Wylie Frydrychowi, Madiraju Praveen, Zeno Franco, Katinka Hooyer, Priyanka Annapureddy, Sheikh Iqbal Ahamed","doi":"10.1109/COMPSAC48688.2020.00-12","DOIUrl":null,"url":null,"abstract":"Millions of US service members have been deployed to Iraq and Afghanistan over the past decade. When returning home, many veterans experience difficulties reintegrating into civilian society. Veterans are often faced with challenges finding employment, completing higher education and reconnecting to friends and family. These challenges often result in or exacerbate existing mental health issues. Post-Traumatic Stress Disorder (PTSD) is a mental disorder that impacts between 15-20% of veterans. It is a national priority to find innovative solutions to PTSD experienced by veterans returning from their duty. There is very little research on identifying Angry Outburst (AOB) specific pre-crisis data in social media posts, within the veteran community, as a preventative measure against escalated at-risk behavior and negative outcomes. In this paper, we outline a threephase approach to identify veteran AOB specific pre-crisis text data. The key objective of our study is to examine twitter posts to reveal how anger is expressed by both the veteran population and civilians. We identify a lexicon of terms that are more common among veterans with PTSD prone to AOB. Our study emphasizes the difference in language used on social media between both veteran and civilian population. We expand the knowledge base of AOB specific pre-crisis events on social media within veteran communities. This research will contribute to a broader study on building preventative mHealth systems to combat PTSD in veterans.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"265 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Understanding Veterans Expression of Anger Using Social Media Analysis\",\"authors\":\"Nadiyah Johnson, Joseph Coelho, Md Fitrat Hossain, Thomas Kissane, Wylie Frydrychowi, Madiraju Praveen, Zeno Franco, Katinka Hooyer, Priyanka Annapureddy, Sheikh Iqbal Ahamed\",\"doi\":\"10.1109/COMPSAC48688.2020.00-12\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Millions of US service members have been deployed to Iraq and Afghanistan over the past decade. When returning home, many veterans experience difficulties reintegrating into civilian society. Veterans are often faced with challenges finding employment, completing higher education and reconnecting to friends and family. These challenges often result in or exacerbate existing mental health issues. Post-Traumatic Stress Disorder (PTSD) is a mental disorder that impacts between 15-20% of veterans. It is a national priority to find innovative solutions to PTSD experienced by veterans returning from their duty. There is very little research on identifying Angry Outburst (AOB) specific pre-crisis data in social media posts, within the veteran community, as a preventative measure against escalated at-risk behavior and negative outcomes. In this paper, we outline a threephase approach to identify veteran AOB specific pre-crisis text data. The key objective of our study is to examine twitter posts to reveal how anger is expressed by both the veteran population and civilians. We identify a lexicon of terms that are more common among veterans with PTSD prone to AOB. Our study emphasizes the difference in language used on social media between both veteran and civilian population. We expand the knowledge base of AOB specific pre-crisis events on social media within veteran communities. This research will contribute to a broader study on building preventative mHealth systems to combat PTSD in veterans.\",\"PeriodicalId\":430098,\"journal\":{\"name\":\"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"volume\":\"265 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMPSAC48688.2020.00-12\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMPSAC48688.2020.00-12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

在过去十年中,数百万美国军人被部署到伊拉克和阿富汗。许多退伍军人回国后很难重新融入平民社会。退伍军人经常面临着找工作、完成高等教育以及与朋友和家人重新联系的挑战。这些挑战往往导致或加剧现有的心理健康问题。创伤后应激障碍(PTSD)是一种影响15-20%退伍军人的精神障碍。为退伍军人从战场归来后所经历的创伤后应激障碍寻找创新的解决方案是国家的首要任务。很少有研究在退伍军人社区的社交媒体帖子中识别愤怒爆发(AOB)特定的危机前数据,作为预防升级的风险行为和负面结果的措施。在本文中,我们概述了一种三个阶段的方法来识别资深AOB特定的危机前文本数据。我们研究的主要目的是研究推特帖子,以揭示退伍军人和平民如何表达愤怒。我们确定了一个术语的词汇,更常见的退伍军人PTSD倾向于AOB。我们的研究强调了退伍军人和平民在社交媒体上使用语言的差异。我们在退伍军人社区的社交媒体上扩展了AOB特定危机前事件的知识库。这项研究将有助于建立预防性移动医疗系统以对抗退伍军人的创伤后应激障碍的更广泛研究。
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
Understanding Veterans Expression of Anger Using Social Media Analysis
Millions of US service members have been deployed to Iraq and Afghanistan over the past decade. When returning home, many veterans experience difficulties reintegrating into civilian society. Veterans are often faced with challenges finding employment, completing higher education and reconnecting to friends and family. These challenges often result in or exacerbate existing mental health issues. Post-Traumatic Stress Disorder (PTSD) is a mental disorder that impacts between 15-20% of veterans. It is a national priority to find innovative solutions to PTSD experienced by veterans returning from their duty. There is very little research on identifying Angry Outburst (AOB) specific pre-crisis data in social media posts, within the veteran community, as a preventative measure against escalated at-risk behavior and negative outcomes. In this paper, we outline a threephase approach to identify veteran AOB specific pre-crisis text data. The key objective of our study is to examine twitter posts to reveal how anger is expressed by both the veteran population and civilians. We identify a lexicon of terms that are more common among veterans with PTSD prone to AOB. Our study emphasizes the difference in language used on social media between both veteran and civilian population. We expand the knowledge base of AOB specific pre-crisis events on social media within veteran communities. This research will contribute to a broader study on building preventative mHealth systems to combat PTSD in veterans.
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
期刊最新文献
The European Concept of Smart City: A Taxonomic Analysis An Early Warning System for Hemodialysis Complications Utilizing Transfer Learning from HD IoT Dataset A Systematic Literature Review of Practical Virtual and Augmented Reality Solutions in Surgery Optimization of Parallel Applications Under CPU Overcommitment A Blockchain Token Economy Model for Financing a Decentralized Electric Vehicle Charging Platform
×
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