OBJECTIVES Using publicly available, geotagged Twitter data, we created neighborhood indicators for happiness, food and physical activity for three large counties: Salt Lake, San Francisco and New York. METHODS We utilize 2.8 million tweets collected between February-August 2015 in our analysis. Geo-coordinates of where tweets were sent allow us to spatially join them to 2010 census tract locations. We implemented quality control checks and tested associations between Twitter-derived variables and sociodemographic characteristics. RESULTS For a random subset of tweets, manually labeled tweets and algorithm labeled tweets had excellent levels of agreement: 73% for happiness; 83% for food, and 85% for physical activity. Happy tweets, healthy food references, and physical activity references were less frequent in census tracts with greater economic disadvantage and higher proportions of racial/ethnic minorities and youths. CONCLUSIONS Social media can be leveraged to provide greater understanding of the well-being and health behaviors of communities-information that has been previously difficult and expensive to obtain consistently across geographies. More open access neighborhood data can enable better design of programs and policies addressing social determinants of health.

中文翻译：

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利用带有地理标记的 Twitter 数据来检查邻里幸福感、饮食和身体活动

目标 使用公开可用的、地理标记的 Twitter 数据，我们为三个大县创建了幸福、食物和体育活动的社区指标：盐湖城、旧金山和纽约。方法 我们在分析中使用了 2015 年 2 月至 8 月期间收集的 280 万条推文。推文发送地点的地理坐标允许我们在空间上将它们连接到 2010 年人口普查区的位置。我们实施了质量控制检查并测试了 Twitter 衍生变量与社会人口学特征之间的关联。结果 对于推文的随机子集，手动标记的推文和算法标记的推文具有极好的一致性：73% 的幸福；83% 用于食物，85% 用于身体活动。快乐的推文，健康的食物参考，在经济劣势更大、种族/族裔少数群体和青年比例较高的人口普查区中，体育活动的提及频率较低。结论 可以利用社交媒体更好地了解社区的福祉和健康行为，而这些信息以前在跨地域一致地获取是困难且昂贵的。更开放的社区数据可以更好地设计解决健康问题的社会决定因素的计划和政策。