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}
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.