{"title":"通过分析和过滤Twitter数据对流感爆发进行分类","authors":"Elizabeth Healy, Husna Siddiqui, Aspen Olmsted","doi":"10.23919/ICITST.2017.8356381","DOIUrl":null,"url":null,"abstract":"This paper uses twitter streaming and filtering techniques to determine which cities the flu is most prevalent in real time. The Twitter streaming API was used to collect data and filter using keywords and location. Our results show that more heavily populated cities have more cases of the flu.","PeriodicalId":440665,"journal":{"name":"2017 12th International Conference for Internet Technology and Secured Transactions (ICITST)","volume":"21 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Classifying influenza outbreaks by analyzing and filtering Twitter data\",\"authors\":\"Elizabeth Healy, Husna Siddiqui, Aspen Olmsted\",\"doi\":\"10.23919/ICITST.2017.8356381\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper uses twitter streaming and filtering techniques to determine which cities the flu is most prevalent in real time. The Twitter streaming API was used to collect data and filter using keywords and location. Our results show that more heavily populated cities have more cases of the flu.\",\"PeriodicalId\":440665,\"journal\":{\"name\":\"2017 12th International Conference for Internet Technology and Secured Transactions (ICITST)\",\"volume\":\"21 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 12th International Conference for Internet Technology and Secured Transactions (ICITST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/ICITST.2017.8356381\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 12th International Conference for Internet Technology and Secured Transactions (ICITST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICITST.2017.8356381","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Classifying influenza outbreaks by analyzing and filtering Twitter data
This paper uses twitter streaming and filtering techniques to determine which cities the flu is most prevalent in real time. The Twitter streaming API was used to collect data and filter using keywords and location. Our results show that more heavily populated cities have more cases of the flu.
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