Manuel Rodríguez-Martínez, Cristian C Garzón-Alfonso
{"title":"Twitter健康监测(THS)系统。","authors":"Manuel Rodríguez-Martínez, Cristian C Garzón-Alfonso","doi":"10.1109/BigData.2018.8622504","DOIUrl":null,"url":null,"abstract":"<p><p>We present the Twitter Health Surveillance (THS) application framework. THS is designed as an integrated platform to help health officials collect tweets, determine if they are related with a medical condition, extract metadata out of them, and create a big data warehouse that can be used to further analyze the data. THS is built atop open source tools and provides the following value added services: Data Acquisition, Tweet Classification, and Big Data Warehousing. In order to validate THS, we have created a collection of roughly twelve thousands labelled tweets. These tweets contain one or more target medical terms, and the labels indicate if the tweet is related or not to a medical condition. We used this collection to test various models based on LSTM and GRU recurrent neural networks. Our experiments show that we can classify tweets with 96% precision, 92% recall, and 91% F1 score. These results compare favorably with recent research on this area, and show the promise of our THS system.</p>","PeriodicalId":74501,"journal":{"name":"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data","volume":"2018 ","pages":"1647-1654"},"PeriodicalIF":0.0000,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/BigData.2018.8622504","citationCount":"10","resultStr":"{\"title\":\"Twitter Health Surveillance (THS) System.\",\"authors\":\"Manuel Rodríguez-Martínez, Cristian C Garzón-Alfonso\",\"doi\":\"10.1109/BigData.2018.8622504\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>We present the Twitter Health Surveillance (THS) application framework. THS is designed as an integrated platform to help health officials collect tweets, determine if they are related with a medical condition, extract metadata out of them, and create a big data warehouse that can be used to further analyze the data. THS is built atop open source tools and provides the following value added services: Data Acquisition, Tweet Classification, and Big Data Warehousing. In order to validate THS, we have created a collection of roughly twelve thousands labelled tweets. These tweets contain one or more target medical terms, and the labels indicate if the tweet is related or not to a medical condition. We used this collection to test various models based on LSTM and GRU recurrent neural networks. Our experiments show that we can classify tweets with 96% precision, 92% recall, and 91% F1 score. These results compare favorably with recent research on this area, and show the promise of our THS system.</p>\",\"PeriodicalId\":74501,\"journal\":{\"name\":\"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data\",\"volume\":\"2018 \",\"pages\":\"1647-1654\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1109/BigData.2018.8622504\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BigData.2018.8622504\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2019/1/24 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigData.2018.8622504","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/1/24 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
We present the Twitter Health Surveillance (THS) application framework. THS is designed as an integrated platform to help health officials collect tweets, determine if they are related with a medical condition, extract metadata out of them, and create a big data warehouse that can be used to further analyze the data. THS is built atop open source tools and provides the following value added services: Data Acquisition, Tweet Classification, and Big Data Warehousing. In order to validate THS, we have created a collection of roughly twelve thousands labelled tweets. These tweets contain one or more target medical terms, and the labels indicate if the tweet is related or not to a medical condition. We used this collection to test various models based on LSTM and GRU recurrent neural networks. Our experiments show that we can classify tweets with 96% precision, 92% recall, and 91% F1 score. These results compare favorably with recent research on this area, and show the promise of our THS system.