词汇热量计：通过社交媒体上的热量输入和输出来衡量公共健康
The Lexicocalorimeter: Gauging public health through caloric input and output on social media
摘要：
我们提出并开发了一个Lexicocalorimeter：一个在线的，交互式的工具，用于测量社交媒体和其他大型文本的“卡路里含量”。我们通过构建广泛但可改进的食物和活动相关短语表来做到这一点，并分别为它们分配热量摄入和支出的来源估计。我们表明，对于Twitter，我们天真的措施“热量输入”，“热量输出”，以及这些措施的比例都与美国本土的健康和福祉措施密切相关。在许多情况下，我们的热量平衡指标优于其两个组成量，可以根据特定的健康和福祉指标（例如糖尿病发病率）进行调整，能够提供反映人群健康状况的实时信号，并且有可能与传统的调查数据一起使用在公共政策和集体自我意识的制定中。因为我们的词汇热量计是原则性短语分数的线性叠加，我们还表明，我们可以超越相关性，探索人们在集体细节中谈论的内容，并帮助理解和解释人口规模条件如何变化，这是黑箱类型方法无法实现的能力。
英文摘要：
We propose and develop a Lexicocalorimeter: an online, interactive instrument for measuring the "caloric content" of social media and other large-scale texts. We do so by constructing extensive yet improvable tables of food and activity related phrases, and respectively assigning them with sourced estimates of caloric intake and expenditure. We show that for Twitter, our naive measures of "caloric input", "caloric output", and the ratio of these measures are all strong correlates with health and well-being measures for the contiguous United States. Our caloric balance measure in many cases outperforms both its constituent quantities, is tunable to specific health and well-being measures such as diabetes rates, has the capability of providing a real-time signal reflecting a population's health, and has the potential to be used alongside traditional survey data in the development of public policy and collective self-awareness. Because our Lexicocalorimeter is a linear superposition of principled phrase scores, we also show we can move beyond correlations to explore what people talk about in collective detail, and assist in the understanding and explanation of how population-scale conditions vary, a capacity unavailable to black-box type methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
