Crowdsourcing Health Labels: Inferring Body Weight from Profile Pictures

Ingmar Weber, Yelena Mejova
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引用次数: 25

Abstract

To use social media for health-related analysis, one key step is the detection of health-related labels for users. But unlike transient conditions like flu, social media users are less vocal about chronic conditions such as obesity, as users might not tweet ``I'm still overweight''. As, however, obesity-related conditions such as diabetes, heart disease, osteoarthritis, and even cancer are on the rise, this obese-or-not label could be one of the most useful for studies in public health. In this paper we investigate the feasibility of using profile pictures to infer if a user is overweight or not. We show that this is indeed possible and further show that the fraction of labeled-as-overweight users is higher in U.S. counties with higher obesity rates. Going from public to individual health analysis, we then find differences both in behavior and social networks, for example finding users labeled as overweight to have fewer followers.
众包健康标签:从个人资料图片推断体重
使用社交媒体进行健康相关分析,关键的一步是为用户检测健康相关标签。但与流感等暂时性疾病不同,社交媒体用户对肥胖等慢性疾病的声音较少,因为用户可能不会发推文说“我还是超重”。然而,由于与肥胖相关的疾病,如糖尿病、心脏病、骨关节炎,甚至癌症都在上升,这种肥胖与否的标签可能是公共卫生研究中最有用的标签之一。在本文中,我们研究了使用头像来推断用户是否超重的可行性。我们表明,这确实是可能的,并进一步表明,在肥胖率较高的美国县,超重用户的比例更高。从公共健康分析到个人健康分析,我们发现了行为和社交网络的差异,例如,发现被标记为超重的用户拥有更少的粉丝。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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