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: 1934925
• 负责人: Lisa Singh
• 金额: $ 230.16万
• 依托单位: Georgetown University
• 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 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934925&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“日益融合的研究”项目旨在开发算法和工具，以更好地使用社交媒体数据和其他新形式的公开文本数据，以增进对人类行为和社会的理解。研究团队将整合社会科学、行为科学和计算机科学，以遵循社会科学的设计结构、测量严谨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。尽管该领域正在涌现大量研究，但尚不存在用于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的既定最佳实践。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解释和概括这些数据。具体来说，该项目将（1 ）开发一种详细的混合方法（社交媒体研究迭代方法 - 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，以加强社会和计算机科学领域的研究实践； (2) 使用 IMSMR 制定指南，使用一系列不同的社交媒体数据来回答不同社会和数据科学学科的问题； (3) 测试和完善跨越多个社会、行为和经济学科的不同研究范例的方法和指南； (4) 开发一个共享文本分析研究门户，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生成结构化变量，并遵守社会科学的有效性和可靠性标准。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点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

Parenting online: analyzing information provided by parenting-focused Twitter accounts

在线育儿：分析以育儿为中心的 Twitter 帐户提供的信息

• DOI: 10.1080/15456870.2022.2061713
• 发表时间: 2022-01
• 期刊: Atlantic Journal of Communication
• 影响因子: 1.4
• 作者: Ryan, Rebecca;Davis;Bode, Leticia;Krüger, Jule;Mneimneh, Zeina;Singh, Lisa
• 通讯作者: Singh, Lisa

Inferring #MeToo Experience Tweets using Classic and Neural Models [Inferring #MeToo Experience Tweets using Classic and Neural Models]

推断

• DOI: 10.5220/0011278100003269
• 发表时间: 2022-01
• 期刊: Technology and Applications - DATA
• 作者: Zech, Julianne;Singh, Lisa;Kawintiranon, Kornraphop;Mezey, Naomi;Williams, Jamillah
• 通讯作者: Williams, Jamillah

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: 2023-01
• 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

Traditional and context-specific spam detection in low resource settings

资源匮乏环境中的传统垃圾邮件检测和特定于上下文的垃圾邮件检测

• DOI: 10.1007/s10994-022-06176-x
• 发表时间: 2022-07
• 期刊: Machine Learning
• 影响因子: 7.5
• 作者: Kawintiranon, Kornraphop;Singh, Lisa;Budak, Ceren
• 通讯作者: Budak, Ceren

A Guided Topic-Noise Model for Short Texts

短文本引导式主题噪声模型

• DOI: 10.1145/3485447.3512007
• 发表时间: 2022-04
• 期刊: WWW '22: Proceedings of the ACM Web Conference 2022
• 作者: Churchill, Robert;Singh, Lisa;Ryan, Rebecca;Davis
• 通讯作者: Davis