IBSS: The Spread and Impact of Moral Messages: Machine Learning, Network Evolution, and Behavioral Prediction

在2013年波士顿马拉松悲剧发生后，社交媒体上立即出现了数十万亲社会行为，比如转发献血网站的链接，如何与亲人联系的信息，甚至提出为有需要的人提供食物和住所。这些行为远不是孤立的行为，而是发生在社交网络中，在同情和团结的共同道德信息中发生。这项跨学科的研究项目将研究人们如何应对公共危机，以及道德反应如何塑造社交网络中的这些反应。该项目将在道德心理学、网络社会学、计算机科学等领域贡献新的理论见解和方法论进步。它将加强对道德关切如何通过社会网络传播的理解，并探索处理人类道德决策和群体动态的新理论框架。这些理论框架将指导人工智能技术的发展，以构建道德描述性模型，并将使用情感分析和机器学习的新方法来评估网络中道德关切和社会影响力的理论模型。通过审查影响道德信息传播和参与慈善捐赠等亲社会活动的因素，该项目可能有助于增加紧急情况下个人的福祉。该项目还将促进未来的调查，即社交媒体的公共性和持久性如何提供新的方式来理解和预测社会变化。跨学科的道德科学利用许多不同的方法，如道德基础理论和施瓦茨的价值观圈，开发了经过充分验证的道德关切指标。然而，这一领域的实证研究通常通过在行动发生后很久才收集信息的问卷来评估道德判断。社会学在评估行为发生时做了更多的工作，但使用了更有限的衡量标准。计算机科学的最新创新提供了新的方法来收集关于道德判断的结构和自然环境中的大规模行为以及两者之间的关系的信息。调查人员将使用这些新的基于计算机的方法来检查来自社交媒体的文本，以便检查道德关切和价值观的结构，而不依赖于预设的问卷。他们将调查道德信息和行为传播的网络动态，并将确定社交媒体中的道德内容如何预测个人和社会规模的现实世界行为。研究人员将把机器学习和情绪分析技术与道德认知和社会动力学理论结合起来。他们将探索的问题包括：日常道德判断(在没有研究人员提示的情况下做出)与主流道德心理学理论的一致性如何，以及是否有可能对道德影响如何导致随后的亲社会或反社会行为进行建模和预测。该项目通过NSF跨学科行为和社会科学研究(IBSS)竞赛得到支持。

In the immediate aftermath of the 2013 Boston Marathon tragedy, hundreds of thousands of prosocial acts were evident on social media, such as reposted links for blood donation sites, information regarding how to get in touch with loved ones, and even offers to provide food and shelter for those in need. Far from isolated acts, these behaviors occurred within social networks, amid shared moral messages of empathy and solidarity. This interdisciplinary research project will examine how people respond to public crises and how moral reactions shape these responses in social networks. The project will contribute new theoretical insights and methodological advances in moral psychology, network sociology, computer science, and other fields. It will enhance understanding of how moral concerns spread through social networks and explore new theoretical frameworks dealing with human moral decision making and group dynamics. These theoretical frameworks will guide the development of artificial intelligence techniques for building descriptive models of morality, and the new methods of sentiment analysis and machine learning will be used to assess theoretical models of moral concerns and social influence in networks. By examining factors influencing the spread of moral messages and participation in prosocial activities, such as charitable giving, the project may help increase the well-being of individuals in emergency situations. The project also will facilitate future inquiry into how the public and persistent nature of social media may provide new ways to understand and forecast social change.The interdisciplinary science of morality has developed well-validated measures of moral concerns using a number of different approaches, such as Moral Foundations Theory and Schwartz's Values Circumplex. Empirical research in this field usually has assessed moral judgments via questionnaires gathering information well after actions have occurred, however. Sociology has done more to assess behavior as it occurs but has used even more limited measures. Recent innovations in computer science offer new ways to gather information about the structure of moral judgments and large-scale behavior in natural settings as well as the relationships between the two. The investigators will employ these new computer-based methods to examine texts from social media in order to examine the structure of moral concerns and values without relying on preset questionnaires. They will investigate the network dynamics of the spread of moral messages and behaviors, and they will determine how moral content in social media can predict real-world behavior at both individual and societal scales. The investigators will couple machine learning and sentiment analysis techniques with theories about moral cognition and social dynamics. Among questions they will pursue are how well everyday moral judgments (made without researcher prompting) correspond with dominant psychological theories of morality and whether it is possible to model and predict how moral influence can lead to subsequent prosocial or antisocial behavior. This project is supported through the NSF Interdisciplinary Behavioral and Social Sciences Research (IBSS) competition.