Collaborative Research: SoCS: Analysis of Social Media Driven By Theories of Political Psychology

合作研究：SoCS：政治心理学理论驱动的社交媒体分析

• 批准号: 0968481
• 负责人: William Cohen
• 金额: $ 35.63万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0968481&HistoricalAwards=false
• 关键词: Collaborative; Research; SoCS; Analysis; Social

Understanding how people make decisions in complex settings is crucial in many application areas, including marketing, intelligence analysis, and political decision making. Traditionally, human decision-makers are modeled as rational agents seeking to maximize some mathematical measure of utility. In fact, however, people are overwhelmed in information-rich environments, and have developed cognitive and emotional strategies to navigate such environments. One such strategy is "motivated reasoning", where information is first evaluated subconsciously for emotional content, with the goal of maintaining an existing emotional commitment, and cognitive processing of the information is then conditioned on this emotional evaluation. Political scientists have demonstrated motivated reasoning in evaluation of both candidates and issues. In general, decision-making and information-gathering are strongly influenced by emotion, prior knowledge, and the social communities to which a person belongs. Evidence suggests that accurate models of human decision-making must be complex enough to model not only utility, but prior knowledge and beliefs, human cognitive abilities, and social context. Building such cognitive models requires substantially extending the state-of-the-art in machine learning.In the past, the ability of researchers in political psychology to develop such complex models of decision-making was limited by the amount of data obtainable obtain from surveys or human-subject experiments. The recent explosion of on-line political communities provides an opportunity to overcome this limitation. We will model human behavior for socially-driven information gathering and decision-making tasks - specifically for political decisions - by combining human-subject experiments with analysis of large datasets of social media and social interactions.

了解人们如何在复杂环境中做出决策在许多应用领域至关重要，包括营销，情报分析和政治决策。传统上，人类决策者被建模为理性的代理人，寻求最大限度地提高某些数学效用。然而，事实上，人们在信息丰富的环境中不知所措，并开发了认知和情感策略来驾驭这种环境。 其中一种策略是“动机推理”，即首先下意识地评估信息的情感内容，目的是保持现有的情感承诺，然后对信息的认知处理以这种情感评估为条件。政治学家在评价候选人和问题时都表现出了动机性推理。一般来说，决策和信息收集受到情感、先验知识和个人所属社会群体的强烈影响。有证据表明，人类决策的精确模型必须足够复杂，不仅要模拟效用，还要模拟先验知识和信念、人类认知能力和社会背景。 要建立这样的认知模型，就需要大大扩展机器学习的最新技术。在过去，政治心理学研究人员开发这种复杂的决策模型的能力受到调查或人类受试者实验获得的数据量的限制。最近在线政治社区的激增为克服这一限制提供了机会。我们将通过将人类受试者实验与社交媒体和社交互动的大型数据集分析相结合，为社会驱动的信息收集和决策任务（特别是政治决策）建立人类行为模型。