文章基本信息

标题：Content Analysis of Syndromic Twitter Data
本地全文：下载
作者：Bethany Keffala ; Mike Conway ; Son Doan 等
期刊名称：Online Journal of Public Health Informatics
电子版ISSN：1947-2579
出版年度：2013
卷号：5
期号：1
语种：English
出版社：University of Illinois at Chicago
摘要：We present the results of a content analysis of tweets related to respiratory syndrome. An annotation scheme was developed to differentiate between true positive and false positive tweets, and to quantify more fine-grained information about the content of the tweets. This annotation scheme is general, and as such can be used to aid in surveillance of different syndromes. In addition to finding good separation between true and false positive tweets, results showed that users referencing respiratory syndrome were more likely to discuss their own, current experience than they were to reference another person's symptoms or symptoms not currently being experienced, that expressed sentiment was largely negative, and that there was significant use of expressions of aspiration or hyperbole.