“正确的时间，正确的地点”Twitter上的健康传播:位置信息的价值和准确性%A Burton,Scott H %A Tanner,Kesler W %A Giraud-Carrier,Christophe G %A West,Joshua H %A Barnes,Michael D %+杨百翰大学，计算机科学系计算健康科学研究小组，3361 TMCB，犹他州Provo, 84602，美国，1 801 422 8602，cgc@cs.byu.edu % K Twitter % K GPS定位% K Infodemiology % K监视% K干预% K社交媒体% D原始论文7 15.11.2012 % 9 2012% % J J互联网Res % G英语% X背景:Twitter提供了各种类型的位置数据,包括精确的全球定位系统(GPS)坐标,可用于infoveillance和Infodemiology(即研究和监测的网络健康信息),健康传播和干预措施。尽管有潜力，Twitter的位置信息还没有被很好地理解或记录，限制了它的公共卫生效用。目的:本研究的目的是记录和描述Twitter中可用的各种类型的位置信息。描述了可以从Twitter用户确定的不同类型的位置数据。这些信息对未来研究这些位置数据的可用性、可用性和局限性至关重要。方法:利用Twitter的应用程序编程接口(API)直接从Twitter上收集位置数据。在2011年10月和11月的两周内，我们收集了Twitter允许的最大推文(占总推文的1%)。最终的数据集包括来自950万独立用户的2380万条推文。 Frequencies for each of the location options were calculated to determine the prevalence of the various location data options by region of the world, time zone, and state within the United States. Data from the US Census Bureau were also compiled to determine population proportions in each state, and Pearson correlation coefficients were used to compare each state’s population with the number of Twitter users who enable the GPS location option. Results: The GPS location data could be ascertained for 2.02% of tweets and 2.70% of unique users. Using a simple text-matching approach, 17.13% of user profiles in the 4 continental US time zones were able to be used to determine the user’s city and state. Agreement between GPS data and data from the text-matching approach was high (87.69%). Furthermore, there was a significant correlation between the number of Twitter users per state and the 2010 US Census state populations (r ≥ 0.97, P < .001). Conclusions: Health researchers exploring ways to use Twitter data for disease surveillance should be aware that the majority of tweets are not currently associated with an identifiable geographic location. Location can be identified for approximately 4 times the number of tweets using a straightforward text-matching process compared to using the GPS location information available in Twitter. Given the strong correlation between both data gathering methods, future research may consider using more qualitative approaches with higher yields, such as text mining, to acquire information about Twitter users’ geographical location. %M 23154246 %R 10.2196/jmir.2121 %U //www.mybigtv.com/2012/6/e156/ %U https://doi.org/10.2196/jmir.2121 %U http://www.ncbi.nlm.nih.gov/pubmed/23154246