CAREER: Interacting Dynamic Bayesian Models for Social Behavior and Reasoning

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。人们一生中花了大量的时间社交，或与他人互动。社会互动是我们大多数活动的固有组成部分，因此，理解社会互动是理解人类行为的基本组成部分。随着人们在网上花费的时间迅速增长，社交互动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着时间的推移的互动。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，并帮助高中生学习更多关于数据分析的知识。该项目的主要研究者还保持了积极参与组织妇女在机器学习研讨会，以确保妇女在干的进步坚定的承诺。这项研究的重点是了解互动本身的底层结构。研究小组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种底层结构。更具体地说，该项目开发：（1）交互动态贝叶斯方法，可用于对社会互动进行建模，共同捕捉个体之间互动的时间动态和语言内容，并发现个体的潜在属性，如权力和影响力，或欺凌者和受害者等角色;以及（2）分析流行病学社交网络数据的方法。

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.