Can Twitter be used to predict county excessive alcohol consumption rates?
Can Twitter be used to predict county excessive alcohol consumption rates?
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DOI：
10.1371/journal.pone.0194290
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发表时间：
2018-04-04
期刊：
影响因子：
3.7
通讯作者：
Schwartz, H. Andrew
中科院分区：
文献类型：
--
作者：
Curtis, Brenda;Giorgi, Salvatore;Schwartz, H. Andrew
ObjectivesThe current study analyses a large set of Twitter data from 1，384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county.MethodsData from over 138 million county-level tweets were analyzed using predictive modeling，differential language analysis，结果Twitter的语言数据捕捉了过度饮酒的横截面模式，超出了社会人口学因素（例如年龄、性别、种族、收入、教育），并且可以用来准确地预测过量饮酒的比率。此外，调解分析发现，Twitter的主题（例如“准备好离开”）可以解释社会经济学和过度饮酒之间的差异。ConclusionsTwitter的数据可以用来预测公共卫生问题，如过度饮酒。使用中介分析与预测建模相结合，可以解释与社会经济地位相关的大部分方差。
ObjectivesThe current study analyzes a large set of Twitter data from 1,384 US counties to determine whether excessive alcohol consumption rates can be predicted by the words being posted from each county.MethodsData from over 138 million county-level tweets were analyzed using predictive modeling, differential language analysis, and mediating language analysis.ResultsTwitter language data captures cross-sectional patterns of excessive alcohol consumption beyond that of sociodemographic factors (e.g. age, gender, race, income, education), and can be used to accurately predict rates of excessive alcohol consumption. Additionally, mediation analysis found that Twitter topics (e.g. 'ready gettin leave') can explain much of the variance associated between socioeconomics and excessive alcohol consumption.ConclusionsTwitter data can be used to predict public health concerns such as excessive drinking. Using mediation analysis in conjunction with predictive modeling allows for a high portion of the variance associated with socioeconomic status to be explained.