GCR: Collaborative Research: The Future of Quantitative Research in Social Science

GCR：协作研究：社会科学定量研究的未来

• 批准号: 1934494
• 负责人: Ceren Budak
• 金额: $ 129.4万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934494&HistoricalAwards=false
• 关键词: GCR; Collaborative; Research; Future; Quantitative

This Growing Convergence Research project aims to develop algorithms and tools to better use social media data and other new forms of publicly available text data to advance understanding of human behavior and society. The research team will integrate across the social, behavioral, and computer sciences to create and adapt computer algorithms and data mining methods in ways that adhere to the design structures, measurement rigor and ethical protections of social science. While much research is emerging in this space, no established best practices exist for designing proper micro- and macro-level studies involving social media and other open-source text data. The research team, representing the breadth of behavioral/social science and computer science, will develop and test methodologies for sampling, validating, and analyzing social media data so that social scientists can easily interpret and generalize from them.Specifically, this project will (1) develop a detailed, hybrid methodology (Iterative Method for Social Media Research - IMSMR) that integrates relevant components of existing social science methodologies with relevant components of the knowledge discovery process to enhance research practices in both social and computer science fields; (2) use IMSMR to establish guidelines for using an array of different social media data to answer questions across different social and data science disciplines; (3) test and refine the methodology and guidelines on different research exemplars that spans multiple social, behavioral, and economic disciplines; and (4) develop a shared text analytic research portal that enables social scientists to generate structured variables using state of the art natural language processing and data mining that adhere to the validity and reliability standards of social science.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.

这个不断增长的融合研究项目旨在开发算法和工具，以更好地利用社交媒体数据和其他新形式的公开文本数据，以促进对人类行为和社会的理解。该研究团队将整合社会，行为和计算机科学，以遵循社会科学的设计结构，测量严格性和道德保护的方式创建和调整计算机算法和数据挖掘方法。虽然在这一领域出现了许多研究，但在设计涉及社交媒体和其他开源文本数据的适当微观和宏观研究方面，还没有既定的最佳实践。该研究团队代表了行为/社会科学和计算机科学的广度，将开发和测试用于采样，验证和分析社交媒体数据的方法，以便社会科学家可以轻松地对其进行解释和概括。具体而言，该项目将（1）开发详细的，混合方法（社交媒体研究的迭代方法- IMSMR）将现有社会科学方法的相关组成部分与知识发现过程的相关组成部分相结合，以加强社会科学和计算机科学领域的研究实践;（2）使用IMSMR建立使用一系列不同社交媒体数据来回答不同社会和数据科学学科问题的指导方针;（3）测试和完善跨多个社会，行为和经济学科的不同研究范例的方法和指导方针;以及（4）开发一个共享的文本分析研究门户网站，使社会科学家能够使用最先进的自然语言处理和数据挖掘来生成结构化变量，这些变量符合有效性，社会科学的可靠性标准。该奖项反映了NSF的法定使命，并且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

Analyzing the impact of missing values and selection bias on fairness

分析缺失值和选择偏差对公平性的影响

• DOI: 10.1007/s41060-021-00259-z
• 发表时间: 2021
• 期刊: International Journal of Data Science and Analytics
• 影响因子: 2.4
• 作者: Wang, Yanchen;Singh, Lisa
• 通讯作者: Singh, Lisa

Text Analytic Research Portals: Supporting Large-Scale Social Science Research

文本分析研究门户：支持大规模社会科学研究

• DOI: 10.1109/bigdata52589.2021.9671696
• 发表时间: 2021
• 期刊: 2021 IEEE International Conference on Big Data (Big Data
• 作者: Singh, Lisa;Padden, Colton;Davis-Kean, Pamela;David, Rabin;Marwadi, Virinche;Ren, Yiqing;Vanarsdall, Rebecca
• 通讯作者: Vanarsdall, Rebecca

Students or Mechanical Turk: Who Are the More Reliable Social Media Data Labelers? [Students or Mechanical Turk: Who Are the More Reliable Social Media Data Labelers?]

学生或机械土耳其人：谁是更可靠的社交媒体数据标签者？

• DOI: 10.5220/0011278600003269
• 发表时间: 2022
• 期刊: Technology and Applications - DATA
• 作者: Singh, Lisa;Vanarsdall, Rebecca;Wang, Yanchen;Gresenz, Carole
• 通讯作者: Gresenz, Carole

Social Media Data - Our Ethical Conundrum. A Quarterly bulletin of the IEEE Computer Society Technical Committee on Database Engineering

社交媒体数据 - 我们的道德难题。

• 发表时间: 2020
• 期刊: A Quarterly bulletin of the IEEE Computer Society Technical Committee on Database Engineering
• 作者: Singh, L;Polyzhou, A;Wang, Y;Farr, J;Gresenz, C.
• 通讯作者: Gresenz, C.

Identifying High-Quality Training Data for Misinformation Detection [Identifying High-Quality Training Data for Misinformation Detection]

识别用于错误信息检测的高质量训练数据 [识别用于错误信息检测的高质量训练数据]

• DOI: 10.5220/0012089000003541
• 发表时间: 2023
• 期刊: Technology and Applications - DATA
• 作者: Haber, Jaren;Kawintiranon, Kornraphop;Singh, Lisa;Chen, Alexander;Pizzo, Aidan;Pogrebivsky, Anna;Yang, Joyce
• 通讯作者: Yang, Joyce