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

RR：Twitter 数据在人口研究中的普遍性和可复制性

• 批准号: 1823633
• 负责人: Guangqing Chi
• 金额: $ 50万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1823633&HistoricalAwards=false
• 关键词: RR; Generalizability; Replicability; Twitter; Data

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.

社交媒体数据有可能实时追踪各种现象，比如灾难或恐怖事件发生后几分钟内感到恐惧的人口比例，或者在一个备受关注的案件中，陪审团宣布裁决后立即出现的愤怒程度。在这些例子中，很难进行实时的实地调查，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。社交媒体数据有潜力克服这些限制。该项目将分析调查加权的应用如何重新平衡Twitter数据样本，并评估这种重新平衡将如何允许对人口行为进行有效的概括。该项目将为今后在利用社会媒体数据进行科学、卫生和应用研究方面取得进展奠定基础，从而使各种各样的推论对制定社会政策有用。该项目的一个关键方面将提供有关实时移民流动准确性的新证据，从而协助与提供援助以应对自然灾害有关的社会政策。该项目将评估Twitter用户在不同人口群体中代表或歪曲人口的程度，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月在美国县一级进行研究，研究期间使用96%的地理标记推文，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，用于输入每个Twitter用户的性别、年龄、种族/民族和居住地；(2)利用这些值来评估县级Twitter样本的代表性，并解释偏差的决定因素；(3)采用概率或非概率调查开发的五种方法来重新加权Twitter样本，并比较它们在产生可用于推断一般人群特征的模型估计中的表现；(4)通过与美国国税局（Internal Revenue Service）的移民数据比较，检验使用Twitter数据估算县一级移民的可行性，以及估算飓风玛丽亚后波多黎各移民到美洲大陆的可行性。对这些移民数据的分析将提供一个新的信息来源，用来实时地、以前所未有的详细地理尺度估计移民流动。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A multi-modal approach towards mining social media data during natural disasters - a case study of Hurricane Irma

• DOI: 10.1016/j.ijdrr.2020.102032
• 发表时间: 2021-01
• 期刊: International journal of disaster risk reduction : IJDRR
• 作者: S. Mohanty;B. Biggers;S. SayedAhmed;Nastaran Pourebrahim;E. Goldstein;Rick L. Bunch;G. Chi;F. Sadri;Tom P. McCoy;A. Cosby

Evaluating the Representativeness in the Geographic Distribution of Twitter User Population

评估 Twitter 用户群体地理分布的代表性

• DOI: 10.1145/3281354.3281360
• 发表时间: 2018
• 期刊: ACM SIGSPATIAL
• 作者: Yin, Junjun;Chi, Guangqing;Van Hook, Jennifer
• 通讯作者: Van Hook, Jennifer

Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets

• DOI: 10.1016/j.rser.2020.109781
• 发表时间: 2020-05-01
• 期刊: RENEWABLE & SUSTAINABLE ENERGY REVIEWS
• 影响因子: 15.9
• 作者: Abdar, Moloud;Basiri, Mohammad Ehsan;Asadi, Somayeh
• 通讯作者: Asadi, Somayeh

Assessing the validity of mobile device data for estimating visitor demographics and visitation patterns in Yellowstone National Park

• DOI: 10.1016/j.jenvman.2022.115410
• 发表时间: 2022-06-10
• 期刊: JOURNAL OF ENVIRONMENTAL MANAGEMENT
• 影响因子: 8.7
• 作者: Liang, Yun;Yin, Junjun;Chi, Guangqing
• 通讯作者: Chi, Guangqing

Using social media user profiles to identify visitor demographics and origins in Yellowstone national park

• DOI: 10.1016/j.jort.2023.100620
• 发表时间: 2023-03
• 期刊: Journal of Outdoor Recreation and Tourism
• 作者: Yun Liang;Junjun Yin;Soyoung Q. Park;Bing Pan;G. Chi;Z. Miller