Twitter Health Surveillance (THS) System.

Manuel Rodríguez-Martínez, Cristian C Garzón-Alfonso
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引用次数: 10

Abstract

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.

Abstract Image

Abstract Image

Abstract Image

Twitter健康监测(THS)系统。
我们提出了Twitter健康监测(THS)应用框架。THS被设计成一个集成平台,帮助卫生官员收集推文,确定它们是否与医疗状况有关,从中提取元数据,并创建一个可用于进一步分析数据的大数据仓库。THS建立在开源工具之上,提供以下增值服务:数据采集、Tweet分类和大数据仓库。为了验证THS,我们创建了一个大约一万二千条带标签推文的集合。这些tweet包含一个或多个目标医学术语,标签指示tweet是否与医疗状况相关。我们使用这个集合来测试基于LSTM和GRU递归神经网络的各种模型。我们的实验表明,我们可以以96%的准确率,92%的召回率和91%的F1分数对tweet进行分类。这些结果与该领域最近的研究结果相比较,表明了我们的三步走系统的前景。
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