The complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets.
The complex relationship of realspace events and messages in cyberspace: case study of influenza and pertussis using tweets.
复制标题
DOI：
10.2196/jmir.2705
复制
发表时间：
2013-10-24
影响因子：
7.4
通讯作者：
Sawyer MH
中科院分区：
文献类型：
--
作者：
Nagel AC;Tsou MH;Spitzberg BH;An L;Gawron JM;Gupta DK;Yang JA;Han S;Peddecord KM;Lindsay S;Sawyer MH
监测在疾病检测中发挥着至关重要的作用，但收集患者数据、向卫生官员报告和编写报告的传统方法既昂贵又耗时。近年来，症状监测工具不断扩展，研究人员能够以最低的成本利用互联网上实时提供的大量数据。存在许多信息监视数据源，但本研究重点关注 Twitter 微博网站的状态更新（推文）。这项研究的目的是探索通过特定关键词推文测量的网络空间消息活动与现实世界中流感和百日咳的发生之间的相互作用。推文按周汇总，并与每周流感样疾病 (ILI) 和每周百日咳发病率进行比较。通过将推文分为 4 类来分析推文类型的潜在影响：不转发、转发、带有 URL Web 地址的推文和不带 URL Web 地址的推文。推文是在 11 个美国城市 17 英里半径范围内收集的，这些城市是根据人口规模和疾病数据的可用性而选择的。流感分析涉及全部 11 个城市。百日咳分析基于距离华盛顿州百日咳爆发最近的两个城市（华盛顿州西雅图和俄勒冈州波特兰）。推文收集结果包括 161,821 条流感推文、6174 条流感推文、160 条百日咳推文和 1167 条百日咳推文。计算推文或推文子组与疾病发生之间的相关系数，并以图形方式呈现趋势。每周汇总的推文与疾病发生之间的相关性差异很大，但在某些领域相对较强。一般来说，流感分析的相关系数比百日咳分析更强。在每项分析中，流感推文与流感样疾病发病率的相关性比流感推文更强，百日咳推文与百日咳发病率的相关性比百日咳推文更强。与转发相比，不转发的推文与疾病发生的相关性更大，并且与主要针对流感推文的 URL 网址相比，没有 URL 网址的推文与实际发病率的相关性更好。这项研究表明，关键词选择不仅在推文与疾病发生的相关性方面发挥着重要作用，而且用于分析的推文子组也很重要。这项探索性工作显示了使用推文进行信息监视的潜力，但需要继续努力进一步完善该领域的研究方法。
Surveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website. 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. 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. 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. 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.
登录
查看更多内容
影响因子：
3.7
作者：
Chew C;Eysenbach G
通讯作者：
Eysenbach G
影响因子：
4.9
作者：
Scanfeld D;Scanfeld V;Larson EL
通讯作者：
Larson EL
影响因子：
7.6
作者：
Heaivilin, N.;Gerbert, B.;Gibbs, J. L.
通讯作者：
Gibbs, J. L.
DOI：
10.1056/nejmp0900702
发表时间：
2009-05-21
期刊：
The New England journal of medicine
影响因子：
--
作者：
Brownstein JS;Freifeld CC;Madoff LC
通讯作者：
Madoff LC
DOI：
10.4269/ajtmh.2012.11-0597
发表时间：
2012-01-01
影响因子：
3.3
作者：
Chunara, Rumi;Andrews, Jason R.;Brownstein, John S.
通讯作者：
Brownstein, John S.