CAREER: Interacting Dynamic Bayesian Models for Social Behavior and Reasoning

职业：社会行为和推理的互动动态贝叶斯模型

• 批准号: 1553465
• 负责人: Katherine Heller
• 金额: $ 51.6万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553465&HistoricalAwards=false
• 关键词: CAREER; Interacting; Dynamic; Bayesian; Models

This project develops machine learning methods to analyze online data and illuminate social behavior. People spend a great deal of their lives socializing, or interacting with other people. Social interactions are inherently part of most of our activities, and, therefore, understanding social interactions is a fundamental part of understanding human behavior. As the amount of time people spend online rapidly grows, social interactions are increasingly occurring online. This project uses Dynamic Bayesian models to analyze time series data of social behavior that inherently involves interactions over time. The project provides tools for understanding social behavior and better methods for data analysis. The project trains graduate students and helps high school students learn more about data analysis. The project principal investigator also maintains a strong commitment to actively involving in the organization of Women in Machine Learning Workshop to ensuring the advancement of women in STEM.This research focuses on understanding the underlying structure of the interactions themselves. The research team elucidates this underlying structure through hierarchically modeling the interactions between multiple dynamic processes. More specifically, the project develops: (1) interacting dynamic Bayesian methods that can be used to model social interactions that jointly capture the temporal dynamics and the linguistic content of interactions between individuals, and discover latent attributes of individuals, such as power and influence, or roles such as bullies and victims; and (2) methods for analyzing epidemiological social network data.

该项目开发机器学习方法来分析在线数据并阐明社会行为。人们一生中大部分时间都花在社交或与他人互动上。社交互动本质上是我们大多数活动的一部分，因此，理解社交互动是理解人类行为的基本部分。随着人们上网时间的迅速增加，社交互动也越来越多地发生在网上。 该项目使用动态贝叶斯模型来分析社会行为的时间序列数据，这些数据本质上涉及随时间的交互。该项目提供了理解社会行为的工具和更好的数据分析方法。该项目培训研究生并帮助高中生更多地了解数据分析。该项目首席研究员还坚定致力于积极参与机器学习女性研讨会的组织，以确保女性在 STEM 领域的进步。这项研究的重点是了解交互本身的基本结构。研究团队通过对多个动态过程之间的相互作用进行分层建模来阐明这一底层结构。更具体地说，该项目开发了：（1）交互动态贝叶斯方法，可用于建模社会交互，共同捕获个体之间交互的时间动态和语言内容，并发现个体的潜在属性，例如权力和影响力，或欺凌者和受害者等角色； (2)分析流行病学社交网络数据的方法。