@Article{信息:doi 10.2196 / / jmir。3416，作者=“Nagar, Ruchit and Yuan, Qingyu and Freifeld, Clark C and Santillana, Mauricio and Nojima, Aaron and Chunara, Rumi and Brownstein, John S”，标题=“基于时间和时空视角的纽约市2012-2013流感季节每日地理编码Twitter数据的案例研究”，期刊=“J Med Internet Res”，年=“2014”，月=“10”，日=“20”，量=“16”，数=“10”，页=“e236”，关键词=“流感;推特;纽约市;时空;谷歌流感趋势;infodemiology;移动健康;社交媒体、自然语言处理;背景:Twitter已经显示出在多个国家和不同地理范围内每周预测流感病例的一些有用性。 Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches. Objective: The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases. Methods: From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords ``flu'', ``influenza'', ``gripe'', and ``high fever''. The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis. Results: Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal. Conclusions: While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable. ", issn="1438-8871", doi="10.2196/jmir.3416", url="//www.mybigtv.com/2014/10/e236/", url="https://doi.org/10.2196/jmir.3416", url="http://www.ncbi.nlm.nih.gov/pubmed/25331122" }