Collaborative Research: Using Multi-Modal Digital Footprints to Infer Public Sentiment

社会科学认为，行为主体根据信念行事是不言自明的，然而，梳理出信念和行为之间的相关性并非易事。传统上，社会科学家依靠调查数据来衡量可能表明经济趋势变化的情绪，并通过实验室实验来测试给定操纵信念的行为。然而，调查的规模是出了名的昂贵，很难经常进行，并且存在偏见；实验室实验人为地限制了决策，因此无法捕捉到现实世界行动的复杂依赖关系。该项目将使用有史以来最大的人类行为数据集之一，直接从人们的行为中产生公众情绪的测量结果：在Twitter等平台上表达的在线观点或在新闻网站上发布的评论，代表谷歌搜索查询量的时间序列，以及通话细节记录。利用这些数字足迹，该项目将开发新的社会计算方法，以衡量与经济状况、预期失业率和对国家优先事项的关注相关的公众情绪。更广泛的影响：该项目将为衡量公众情绪的调查提供新的选择，并将对美国人口的在线和现场行为产生前所未有的深入了解。这项研究也有可能帮助公共和私人组织更好地了解客户和选民的动态行为，并根据数据得出的经济趋势做出商业和政策决策。该项目将通过研究生的跨学科培训来提高教育水平。将积极鼓励来自代表性不足群体的研究生和本科生参与该项目。此外，还将设立一个公开网站，详细介绍项目成果。

Social science takes as axiomatic that agents act on beliefs: however, teasing out the correlations between belief and action is no simple feat. Traditionally, social scientists relied on survey data to measure sentiment that might indicate changing economic trends, and on lab experiments to test actions given manipulated beliefs. Surveys, however, are notoriously expensive to scale, difficult to conduct frequently, and possess bias; lab experiments artificially constrain decision-making and thus fail to capture the complex dependencies of real-world actions. This project will generate measurements of public sentiment directly from the actions of people, using one of the largest datasets of human behavior ever studied: online opinions expressed on platforms like Twitter or comments posted on news websites, time series representing the volume of search queries on Google, and call detail records. Using these digital footprints, the project will develop new social-computational measures of public sentiments related to the state of the economy, expected unemployment, and concerns about national priorities. Broader impacts: The project will offer new alternatives to surveys as a measure of public sentiment, and will also generate unprecedented insight into the online and onsite behavior of the American population. This research also has the potential to help public and private organizations better understand the dynamic behaviors of customers and constituents, and to make business and policy decisions informed by economic trends derived from data. The project will enhance education through the interdisciplinary training of graduate students. Both graduate and undergraduate students from underrepresented groups will be actively encouraged to participate in the project. A public website will also be set up detailing the project results.