INSPIRE: Computer Learning of Dynamical Systems to Investigate Cognitive and Motivational Effects of Social Media Use on Political Participation

INSPIRE奖的部分资金由计算机与信息科学与工程系信息与智能系统部的以人为本的计算项目和社会计算系统项目，以及社会、行为与经济科学部行为与认知科学部的社会心理学项目和社会、行为与经济科学部的政治科学项目提供。关于学术价值，这个项目的目标是建立一个跨学科的合作，研究社交媒体对政治行为的影响。首先，从社会心理学和政治学出发，对社交媒体如何、为什么以及何时影响公民对政治参与的认知和动机进行基本假设。其次，这些问题将被表达为来自行为模型的可测试假设。第三，借鉴生物学和计算机科学，该项目采用了近似推理和机器学习的复杂计算方法（适应为分析系统生物学数据而开发的方法）来评估使用超大社交媒体和社交网络数据集的行为模型。当我们考虑到参与选举、抗议、骚乱和其他自发政治事件的数据的丰富性和准确性时，使用社交媒体提供的科学机会是显而易见的。该项目将使用非常适合机器学习方法的系统结构格式构建传入和传出社交媒体消息的综合数据集，这些信息将与社交网络连接信息和大量关于个人及其社交联系人的元数据集成在一起。通过开发新的方法来有效地收集和组合这些数据源，将有可能改变对社会和政治态度和行为的科学研究。每次人们使用社交媒体时，都会留下一个数字足迹，包括交流的内容、时间和对象。通常，只有在人工环境下工作的实验室研究人员才能对这些变量进行如此精确的估计。以前没有研究成功地使用这样的细粒度社会影响数据来预测相应的行为结果，比如参加特定的抗议或集会。数据的结构意味着我们将有关于受访者的面板数据，其中许多可能持续很长时间。此外，调查人员将进行一项小组调查，这对于得出认知和动机过程的因果推论至关重要，社交媒体的使用促进了政治参与。就更广泛的影响而言，本研究将加强研究生和本科生的跨学科训练。这些学生包括心理学、政治学、计算机科学和生物学的学生，也包括来自这些学科中代表性不足的群体的学生。此外，还将为高中生提供参与研究过程的机会。该研究计划将通过利用主要研究人员过去的经验，传播大型代码库、数据库和数据集，与其他科学家分享工作成果，从而促进科学理解的广泛传播（出版前）。最后，研究人员致力于将他们的研究成果提供给公众，并在这方面拥有丰富的经验。

This INSPIRE award is partially funded by Human-Centered Computing Program and by Social-Computational Systems Program both in the Division of Information and Intelligent Systems in the Directorate for Computer & Information Science & Engineering, and by the Social Psychology Program in the Division of Behavioral and Cognitive Sciences and the Political Science Program in the Division of Social and Economic Sciences in the Directorate for Social, Behavioral and Economic Sciences.With regards to intellectual merit, the goal of this project is to forge an interdisciplinary collaboration that examines the impact of social media on political behavior. First, from social psychology and political science, fundamental hypotheses will be developed about how, why and when social media affects citizens' cognition and motivation with respect to political participation. Second, these questions will be expressed as testable hypotheses derived from behavioral models. And third, drawing from biology and computer science, the project adapts sophisticated computational methods of approximate inference and machine learning (adapting methods developed for the analysis of Systems Biology data) to evaluate the behavioral models using extremely large social media and social network datasets. The scientific opportunities afforded by the use of social media are readily apparent when we consider the richness and precision of data on participation in elections, protests, riots, and other spontaneous political events. This project will construct a comprehensive data set of incoming and outgoing social media messages messages using systematically structure formats that are ideally suited to machine learning methods, and this information will be integrated with information on social network connectivity and a vast array of metadata on individuals and their social contacts. By developing new methods to harvest and combine these data sources effectively, it will be possible to transform the scientific study of social and political attitudes and behavior. Every time individuals use social media, they leave behind a digital footprint of what was communicated, when it was communicated, and, to whom it was communicated. Typically, such precise estimates of these variables are available only to laboratory investigators working in artificial settings. No previous study has successfully used fine-grained social influence data such as these to predict consequential behavioral outcomes, such as attendance at a given protest or rally. The structure of the data means that we will have panel data on respondents, many of potentially long duration. In addition, the investigators will conduct a panel survey, which is essential for drawing causal inferences about the cognitive and motivational processes whereby social media use facilitates political participation.With regards to broader impacts, this research will enhance interdisciplinary training for graduate and undergraduate students. These include students in psychology, political science, computer science, and biology and also includes students from groups that are underrepresented in these sciences. In addition, opportunities will be provided for high school students to become involved in the research process. The research program will foster broad dissemination of scientific understanding by leveraging past experience of the principal investigators with disseminating large code-bases, data-bases, and data-sets to share work with other scientists (pre-publication). Finally, the researchers are committed to making their research available to the general public and have extensive experience doing so.