{"title":"通过分析抑郁和抗抑郁的推文找到抑郁的推特用户","authors":"Sudha Tushara Sadasivuni, Yanqing Zhang","doi":"10.1109/INDISCON50162.2020.00039","DOIUrl":null,"url":null,"abstract":"People behave differently for a situation, and this depends on their mental health status at that time. The response to their behavior can be seen in their actions. In this present era, social media attracts people to present their views, and such a process became easier too. Many researchers attempted to study social media postings. Twitter is one such media, where millions of people participate. Twitter has attracted millions of tweeters, and thus have a greater number of tweets are added to its data. We collected and analyzed around two lakh tweets related to keywords of the Kessler Ten-point questionnaire. We also collected antidepressant tweets for our study. The results showed similarity and facilitated a process to identify a user from the tweets.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Finding a Depressive Twitter User by Analyzing Depress and Antidepressant Tweets\",\"authors\":\"Sudha Tushara Sadasivuni, Yanqing Zhang\",\"doi\":\"10.1109/INDISCON50162.2020.00039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"People behave differently for a situation, and this depends on their mental health status at that time. The response to their behavior can be seen in their actions. In this present era, social media attracts people to present their views, and such a process became easier too. Many researchers attempted to study social media postings. Twitter is one such media, where millions of people participate. Twitter has attracted millions of tweeters, and thus have a greater number of tweets are added to its data. We collected and analyzed around two lakh tweets related to keywords of the Kessler Ten-point questionnaire. We also collected antidepressant tweets for our study. The results showed similarity and facilitated a process to identify a user from the tweets.\",\"PeriodicalId\":371571,\"journal\":{\"name\":\"2020 IEEE India Council International Subsections Conference (INDISCON)\",\"volume\":\"201 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE India Council International Subsections Conference (INDISCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDISCON50162.2020.00039\",\"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 India Council International Subsections Conference (INDISCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDISCON50162.2020.00039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Finding a Depressive Twitter User by Analyzing Depress and Antidepressant Tweets
People behave differently for a situation, and this depends on their mental health status at that time. The response to their behavior can be seen in their actions. In this present era, social media attracts people to present their views, and such a process became easier too. Many researchers attempted to study social media postings. Twitter is one such media, where millions of people participate. Twitter has attracted millions of tweeters, and thus have a greater number of tweets are added to its data. We collected and analyzed around two lakh tweets related to keywords of the Kessler Ten-point questionnaire. We also collected antidepressant tweets for our study. The results showed similarity and facilitated a process to identify a user from the tweets.