Bayesian Models of Social Behavior using Online Resources

使用在线资源的社会行为贝叶斯模型

• 批准号: 1048563
• 负责人: Katherine Heller
• 金额: $ 24万
• 依托单位: Heller Katherine A
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048563&HistoricalAwards=false
• 关键词: Bayesian; Models; Social; Behavior; using

People spend a great deal of their lives socializing, or interacting with other people. On a typical day a person might collaborate on a project with their work colleagues, play softball with their teammates, and converse with their family. Social interactions are inherently part of most of our activities, therefore, understanding social interactions is a fundamental part of understanding human behavior. As the amount of time people spend online rapidly grows, social interactions which were once limited to in-person meetings, letters, and telephone calls, are increasingly occurring through the use of online resources such as email, Facebook, and online chats. Social and cognitive scientists who strive to understand human behavior can analyze online interactions to illuminate social behavior in this new setting, and benefit from the wealth of data that it provides. However, social interactions are extremely complex, so analyzing and modeling them is not easy in any setting.Fortunately Bayesian probabilistic methods offer rich, flexible, generative models for data, which can be used to model complex, highly structured, social interactions. In general, Bayesian methods provide a principled framework for reasoning about an uncertain world. Bayesian latent variable models allow us to reason about, or discover, the potentially quite complex, unobserved structure that underlies what we do observe. This research develops methods which discover the unobserved structure necessary to model complex social interactions which occur online, explore group interactions, evaluate how context effects social interactions, and explore social influence. This work has the potential to improve science (e.g. by improving long-distance collaborations), commerce (e.g. by identifying whom businesses should inform about their products), and society at large (e.g. by improving social networking).

人们一生中大部分时间都花在社交或与他人互动上。在典型的一天中，一个人可能会与同事合作完成一个项目，与队友一起打垒球，并与家人交谈。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快速增长，曾经仅限于面对面会议、信件和电话的社交互动越来越多地通过使用电子邮件、Facebook 和在线聊天等在线资源进行。努力了解人类行为的社会和认知科学家可以分析在线互动，以阐明这种新环境中的社会行为，并从其提供的丰富数据中受益。然而，社交互动极其复杂，因此在任何环境下对其进行分析和建模都不容易。幸运的是，贝叶斯概率方法为数据提供了丰富、灵活的生成模型，可用于对复杂、高度结构化的社交互动进行建模。一般来说，贝叶斯方法为推理不确定的世界提供了一个原则框架。贝叶斯潜变量模型使我们能够推理或发现我们所观察到的潜在相当复杂、未观察到的结构。这项研究开发的方法可以发现对在线发生的复杂社交互动进行建模所必需的未观察到的结构，探索群体互动，评估环境如何影响社交互动，并探索社会影响力。这项工作有可能改善科学（例如通过改善远程合作）、商业（例如通过确定企业应该向谁通报其产品）和整个社会（例如通过改善社交网络）。