RIDIR: Collaborative Research: Analytical tools for text based social data integration
RIDIR: Collaborative Research: Analytical tools for text based social data integration
批准号:
1738411
负责人:
Margaret Roberts
金额:
$119.48万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2021-08-31
中文摘要
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英文摘要
When something happens in the world -- such as a natural disaster, an election, a protest, or a policy change -- many types of media record different accounts of the same event. Newspapers, social media posts and government documents all provide unique versions of events stored in different formats. Because each source provides its own perspective, synthesizing these stories vastly increase our ability to learn about both events and the dynamics of the media environment. Yet, social scientists are limited in their capacity to access these myriad perspectives because there are few tools for automatically combining these accounts into one integrated analysis. This project will provide a rich infrastructure for integrating texts from diverse sources documenting the same social phenomenon. Such integration often reveals much about underlying social dynamics.This project will develop a tool to integrate documents with different formats with accounts of the same or closely related events through four main methods. First, the tool will allow users to align documents by topic, while accounting for structural and stylistic differences between documents. Second, the tool will compile different types of documents by a shared event or entity. Third, the tool will allow for user-provided schema to combine semi-structured documents. Last, the tool will facilitate data fusion, by identifying and resolving contradictions from multiple sources. The tool will be sufficiently flexible to fit multiple research purposes, allow for human feedback to assist with integration, and facilitate reproducibility by creating a common resource that can be the basis of future research by a whole community of scholars. The system itself will be applicable to almost any set of unstructured text data and will have broad applicability for questions across the social sciences.
期刊论文(10)
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DOI:
10.1111/ajps.12526
发表时间:
2020
期刊:
American Journal of Political Science
影响因子:
4.2
作者:
[Roberts, Margaret E., Stewart, Brandon M., Nielsen, Richard A.]
通讯作者:
Nielsen, Richard A.
DOI:
10.1145/3442188.3445916
发表时间:
2021-01
期刊:
Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
影响因子:
--
作者:
[Eddie Yang;Margaret E. Roberts]
通讯作者:
Eddie Yang;Margaret E. Roberts
Mass Digitization of Chinese Court Decisions: How to Use Text as Data in the Field of Chinese Law
中国法院判决的大规模数字化:如何在中国法律领域使用文本作为数据
DOI:
10.1086/709916
发表时间:
2020
期刊:
Journal of Law and Courts
影响因子:
1.4
作者:
[Liebman, Benjamin L., Roberts, Margaret E., Stern, Rachel E., Wang, Alice Z.]
通讯作者:
Wang, Alice Z.
Social network of extreme tweeters: a case study
极端推特用户的社交网络:案例研究
DOI:
10.1145/3341161.3342909
发表时间:
2019
期刊:
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
影响因子:
--
作者:
[Zheng, Xiuwen, Gupta, Amarnath]
通讯作者:
Gupta, Amarnath
DOI:
10.1109/escience.2019.00030
发表时间:
2019-05
期刊:
2019 15th International Conference on eScience (eScience)
影响因子:
--
作者:
[Junan Guo;S. Dasgupta;Amarnath Gupta]
通讯作者:
Junan Guo;S. Dasgupta;Amarnath Gupta
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