New Methods

Recent technological advances have given us new ways of analyzing human social behavior, and have created new spaces in which social interactions occur. Our lab leverages these changes to investigate intergroup differences in behavior and cognition.

Natural Language Processing. Psychologists have long been interested in how the written word can reflect underlying psychological states and traits. Thanks to advances in computing power and theory, it is now possible to use a variety of methods to rigorously and flexibly quantify text. We are using these tools in an array of projects, but much of our efforts are devoted to applying these methods to understanding the linguistic mechanisms associated with a written values-affirmation intervention previously shown to reduce the consequences of social identity threat.

Social Network Analysis. Decades of research has shown that having high quality social relationships is one of the biggest determinants of life satisfaction, health, and success in academic and occupational pursuits. In this research, we investigate how individuals’ social relationships interact with their psychology. We use social network analysis to examine a range of questions, from how social ties shift over time in response to stress, psychological threat, and social psychological interventions, to how the composition of individuals’ networks impacts their attitudes, beliefs, and behavior.

Digital Observational Research. A good deal of human social interaction is now documented digitally. This data is present on a vast scale and has the potential to provide a level of insight not previously possible. Our work in this area attempts to document naturally occurring behavior that can help to highlight and explain between-group differences. We are now involved in exploring structural differences in the media with regards to how racially charged events are covered. This work uses a variety of computational methods to measure similarities and differences in media coverage and how this coverage is structured in the broader media landscape.

Sample Publication

Riddle, T.A., Bhagavatula, S., Guo, W., Muresan, S., Cohen, G., Cook, J. & Purdie-Vaughns, V. (2015). Mining a written values affirmation to identify the unique linguistic features of stigmatized groups. In Proceedings of the 8th International Conference on Educational Data Mining. International Educational Data Mining Society. | download pdf

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