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.
