@Article{信息:doi 10.2196 / / jmir。2705，作者="Nagel, Anna C和Tsou, Ming-Hsiang和Spitzberg, Brian H和An, Li和Gawron, J Mark和Gupta, Dipak K和Yang, Jiue-An和Han, Su和Peddecord, K Michael和Lindsay, Suzanne和Sawyer, Mark H"，标题="网络空间真实空间事件和信息的复杂关系:使用推文研究流感和百日咳"，期刊="J Med Internet Res"，年="2013"，月="Oct"，日="26"，卷="15"，数="10"，页="e237"，关键词="推特;infoveillance;infodemiology;网络空间;症状监测;流感;百日咳;背景:监测在疾病检测中起着至关重要的作用，但传统的收集患者数据、向卫生官员报告和编写报告的方法既昂贵又耗时。近年来，症状监测工具得到了扩展，研究人员能够以最低的成本利用互联网上实时可用的大量数据。 Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website. Objective: The aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address. Methods: Tweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically. Results: Correlations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets. Conclusions: This study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field. ", issn="14388871", doi="10.2196/jmir.2705", url="//www.mybigtv.com/2013/10/e237/", url="https://doi.org/10.2196/jmir.2705", url="http://www.ncbi.nlm.nih.gov/pubmed/24158773" }