RI: Small: Modeling Co-Decisions: A Computational Framework Using Language and Metadata

RI：小型：共同决策建模：使用语言和元数据的计算框架

• 批准号: 2008761
• 负责人: Philip Resnik
• 金额: $ 43.1万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008761&HistoricalAwards=false
• 关键词: RI; Small; Modeling; Co; Decisions

In many settings, entire groups are presented with a decision -- for example, a set of legislators can be presented with a bill to vote on, a set of scientific authors can decide whether or not to cite a piece of research in their papers, or a set of social media users might decide whether or not to share a piece of online content. It is very standard to look independently at choices made by individuals, but a more complete scientific understanding of decision-making can be obtained by looking at the decision process in terms of whether individuals will make the same decision or not, taking into account what the individual deciders do and do not have in common. This project is looking at that question by developing new computational methods to help better understand what goes into the decisions people make.The project begins with computational models of co-voting in political contexts, moving from ''how does an individual vote, and why?'' to ''do these individuals vote the same way, and why?''. It generalizes and extends such models, going beyond established factors such as party and state, by enabling incorporation of unstructured language from bills to characterize the issues under consideration and to incorporate analysis and comparison of individuals' language. The extended framework will be validated by demonstrating improved predictive performance on datasets derived from proceedings of the U.S. Congress, making possible direct evaluation against prior work and enabling new substantive analyses of political rhetoric and decision making. In the process, the project also develops richer analysis of individuals' language using techniques that identify interpretable, task-relevant language and by incorporating recently developed methods for incorporating covariates into topic analysis. These advances will be validated by incorporating them within the extended co-voting framework, and will also contribute to the investigation of substantive questions about Congressional decision-making. Finally, the project will address more general use cases by applying the approach beyond the political domain, moving from modeling of co-voting to modeling co-decisions, where a decision is a generalized vote. The generalized model will be validated via application to the problem of scientific citation recommendation.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.

在许多情况下，整个团体都会做出决定 - 例如，可以向一组立法者提出一项法案进行投票，一组科学作者可以决定是否在论文中引用一项研究，或者一组社交媒体用户可以决定是否共享一块在线内容。独立看个人做出的选择是非常标准的，但是可以通过考虑个人是否会做出相同的决策来获得对决策的更完整的科学理解，从而考虑了个人决定者的所作所为和没有共同之处。 该项目正在通过开发新的计算方法来研究这个问题，以帮助更好地了解人们做出的决策是什么。该项目始于在政治背景下共同投票的计算模型，从“个人投票如何，为什么？”到'这些人以同样的方式投票，以及为什么？''。 它通过使非结构化语言从法案中纳入所考虑的问题并结合了个人语言的分析和比较，从而超越了诸如政党和国家等既定因素，例如政党和州等既定因素。扩展框架将通过证明从美国国会会议录中得出的数据集进行改进的预测性能来验证，从而使对先前工作的直接评估成为可能的直接评估，并对政治言论和决策做出的新实质性分析进行了新的实质性分析。 在此过程中，该项目还使用识别可解释的，与任务相关的语言的技术对个人语言进行了更丰富的分析，并通过将最近开发的方法纳入了将协变量纳入主题分析的方法。这些进步将通过将它们纳入扩展的共投票框架中来验证，并将有助于调查有关国会决策的实质性问题。 最后，该项目将通过运用政治领域以外的方法来解决更一般的用例，从共同投票的建模到建模共同决策，在此决定是一般性的投票。广义模型将通过应用于科学引文建议的问题进行验证。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。

项目成果

Natural Language Decompositions of Implicit Content Enable Better Text Representations

隐式内容的自然语言分解可实现更好的文本表示

• DOI: 10.18653/v1/2023.emnlp-main.815
• 发表时间: 2023
• 期刊: Association for Computational Linguistics
• 作者: Hoyle, Alexander;Sarkar, Rupak;Goel, Pranav;Resnik, Philip
• 通讯作者: Resnik, Philip

A Step-by-Step Protocol for Curation of Topic Models by Subject Matter Experts

由主题专家策划主题模型的分步协议

• 发表时间: 2022
• 期刊: Conference on New Directions in Analyzing Text as Data (TADA
• 作者: Resnik, Philip;Goel, Pranav;Hoyle, Alexander;Sarkar, Rupak;Hagedorn, Josh;Gearing, Maeve;Bruce, Carol
• 通讯作者: Bruce, Carol

Mainstream news articles co-shared with fake news buttress misinformation narratives

主流新闻文章与假新闻共同分享支持错误信息叙述

• 发表时间: 2023
• 期刊: International AAAI Conference on Web and Social Media (ICWSM
• 作者: Goel, Pranav;Lazer, David;Resnik, Philip
• 通讯作者: Resnik, Philip

Voting the District or Talking the District?

投票选区还是谈论选区？

• 发表时间: 2022
• 期刊: Women in Legislative Studies
• 作者: Gaynor, SoRelle;Miler, Kristina;Resnik, Philip;Goel, Pranav;Hoyle, Alexander
• 通讯作者: Hoyle, Alexander