RR: The Generalizability and Replicability of Twitter Data for Population Research

社交媒体数据有可能在真实的时间内跟踪现象，例如灾难或恐怖事件发生后几分钟内感到恐惧的人口比例，或者在高度宣传的案件中陪审团裁决宣布后立即愤怒的程度。在上述每一个例子中，都很难在真实的时间内进行实地调查，即使在几天后接受采访，受访者也可能无法重建他们在事件发生时的感受或行为。社交媒体数据有可能克服这些限制。该项目将分析调查加权的应用如何重新平衡Twitter数据的样本，并评估这种重新平衡将如何有效地概括人口行为。该项目将为未来在科学、健康和应用研究中使用社交媒体数据的进步奠定基础，从而允许在社会政策制定中进行各种有用的推断。 该项目的一个关键方面将提供新的证据，说明移徙流动在真实的时间内的准确性，从而协助制定与应对自然灾害提供援助有关的社会政策。 该项目将评估Twitter用户在多大程度上代表或歪曲不同人口群体的人口，并测试开发权重的可行性，当应用于Twitter数据时，使结果更能代表潜在人口。该项目于2014年1月至2017年12月在美国县级进行研究，在研究期间使用96%的地理标记推文，并在一个月内使用100%的推文。该项目将：（1）扩展和完善现有的方法，以估算每个Twitter用户的性别，年龄，种族/民族和居住县;（2）使用这些值来评估Twitter样本在县一级的代表性，并解释偏见的决定因素;（3）采用五种方法，为概率或非概率概率调查，以重新加权Twitter样本，并比较它们在产生模型估计中的表现，这些模型估计可用于推断一般人群的特征;以及（4）通过与美国国税局的移民数据进行比较，测试使用Twitter数据来估计县级移民的可行性，以及估计飓风玛丽亚之后前往大陆的波多黎各移民。这些移民数据的分析将提供一个新的信息来源，以估计移民流量在真实的时间和前所未有的详细的地理scales.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

Social media data have the potential to track phenomena in real time, such as percentage of the population fearful in the minutes after a disaster or terrorist event, or the degree of anger immediately after the announcement of a jury verdict in a highly publicized case. In each of these examples, it would be difficult to conduct a field survey in real time, and respondents may not be able to reconstruct how they felt or behaved at the time of the event, even if interviewed just a few days later. Social media data have the potential to overcome these limitations. This project will analyze how the application of survey weighting can rebalance samples of Twitter data, and assesses how well this rebalancing will allow valid generalizations about population behaviors. The project will provide a foundation for future advances in the use of social media data for scientific, health, and applied research, thus permitting a wide variety of inferences useful in social policy formulation. A key aspect of the project will provide new evidence regarding the accuracy of migration flows in real time, thus assisting social policy relevant to providing assistance in response to natural disasters. This project will evaluate the extent to which Twitter users represent or misrepresent the population across different demographic groups and test the feasibility of developing weights that, when applied to Twitter data, make the results more representative of the underlying population. The project conducts the research at the county level in the United States from January 2014-December 2017, using 96% geotagged tweets in the study period and 100% tweets in one month. The project will: (1) extend and refine existing methods for imputing the gender, age, race/ethnicity, and county of residence of each Twitter user; (2) use these values to assess the representativeness of Twitter samples at the county level and explain the determinants of biases; (3) adapt five methods developed for probability or non-probability surveys to reweight Twitter samples and compare their performance in producing model estimates that can be used to infer characteristics of the general population; and (4) test the feasibility of using Twitter data to estimate migration at the county level by comparing to the Internal Revenue Service migration data, as well as estimate Puerto Rico migrants to the continent after Hurricane Maria. Analysis of these migration data will provide a new source of information with which to estimate migration flows in real time and at unprecedentedly detailed geographic scales.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.