揭示推特上与食物相关的讨论与社区特征之间的关系

V.G.Vinod Vydiswaran, Daniel M. Romero, Xinyan Zhao, D. Yu, Iris N. Gomez-Lopez, Jin Xiu Lu, Bradley E. Iott, A. Baylin, E. Jansen, P. Clarke, V. Berrocal, R. Goodspeed, T. Veinot
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引用次数: 16

摘要

目的减少基于社区的健康差异的举措需要获得有关健康行为及其决定因素的有意义、及时和当地的信息。我们检验了Twitter作为社区层面的饮食选择和态度分析信息来源的有效性。我们分析了普查区层面上与食物相关的推文中的“健康”商和情绪,并将它们与社区特征和健康结果联系起来。我们分析了影响最富裕和最不富裕地区之间食品健康差异的关键字,并对tweet随机样本的内容进行了定性分析。结果:在与食物相关的推文中,健康和情绪与富裕程度、劣势、种族、年龄、美国人口密度和肥胖相关疾病的死亡率之间存在显著的相关性,尽管相关性很弱。对影响食物健康差异的关键词的分析显示,在不太富裕的地区,高饱和脂肪的食物(如披萨、培根、薯条)被提及的频率更高。与食物相关的讨论指的是活动(吃、喝、烹饪)、吃食物的地点,以及积极的(情感、渴望、享受)和消极的态度(不喜欢、个人挣扎、抱怨)。基于推特的健康得分与线下现象在预期方向上有很大的相关性。与传统调查相比,社交媒体提供的资源密集型数据收集方法更少。Twitter可以帮助告知关注食物消费驱动因素的地方卫生项目,也可以告知关注态度和食物环境的干预措施。Twitter在社区层面上提供了关于食物相关行为和态度的微弱但重要的信号,表明其在告知当地减少健康差距的努力方面可能有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics
Abstract Objective Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes. Materials and Methods We analyzed the “healthiness” quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes. We analyzed keywords driving the differences in food healthiness between the most and least-affluent tracts, and qualitatively analyzed contents of a random sample of tweets. Results Significant, albeit weak, correlations existed between healthiness and sentiment in food-related tweets and tract-level measures of affluence, disadvantage, race, age, U.S. density, and mortality from conditions associated with obesity. Analyses of keywords driving the differences in food healthiness revealed foods high in saturated fat (eg, pizza, bacon, fries) were mentioned more frequently in less-affluent tracts. Food-related discussion referred to activities (eating, drinking, cooking), locations where food was consumed, and positive (affection, cravings, enjoyment) and negative attitudes (dislike, personal struggles, complaints). Discussion Tweet-based healthiness scores largely correlated with offline phenomena in the expected directions. Social media offer less resource-intensive data collection methods than traditional surveys do. Twitter may assist in informing local health programs that focus on drivers of food consumption and could inform interventions focused on attitudes and the food environment. Conclusions Twitter provided weak but significant signals concerning food-related behavior and attitudes at the neighborhood level, suggesting its potential usefulness for informing local health disparity reduction efforts.
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