Networks & Computational Social Science
Social networks are an informal form of organization that unveil power, control, and meaning in social structures. Increasingly digitized social interactions promise new social theorization, but accurately studying networks requires creative methods. Dr. Santana develops computational techniques for accurately capturing and analyzing social data with special attention to how computational social science can empower under-represented communities.
Publications & Projects
How Machine Learning Is Reviving Sociological Theorization by J. Santana and Laura K. Nelson. 2024.
in Oxford Handbook of the Sociology of Machine Learning. doi: https://doi.org/10.1093/oxfordhb/9780197653609.013.35
As machine learning (ML) algorithms get more sophisticated, ML enhances the role of theory insociology. ML, despite its capacity to process data in complex ways, by denition only draws on limitedrepresentations of social systems. Humans have direct observational and experiential access to thesesystems, along with broad worldviews and tacit knowledge that make it possible to translate models ofsystems into knowledge of systems. The integration of the limited representational view of ML and theexpansive human worldview is transforming sociological knowledge production. ML revitalizes theory insociology in two key ways: by shifting the focus of theorizing from a priori to a posteriori and bynecessitating ongoing interpretation and theorizing by scholars at every stage of the research process,thus integrating theory throughout research design. A creole computational social science can blendknowledge from multiple disciplines to enhance the knowledge-generating process overall.
“Investor Commitment to Serial Entrepreneurs: A Multilayer Network Analysis.”
Santana, J., Raine Hoover, and Meera Vengadasubbu. 2016. Social Networks 48:256-269.
Social networks are complex systems composed of interdependent organizations and people with diverse network structures. Understanding network dynamics, such as exchange commitment, requires a methodological toolkit that does not assume away complexity. In this study, we extend a technique for analyzing longitudinal, multilayer network data called network alignment. We introduce a novel metric – intersect proportions – for analyzing similarity between divergent graphs. We demonstrate the application of network alignment and intersect proportions to the context of investor commitment to startups and entrepreneurs. Using this technique, we are able to disentangle exchange commitment across complex networks.
“Online Field Experiments: Studying Social Interactions in Context.”
Parigi, Paolo, Jessica J. Santana, and Karen S. Cook. Social Psychology Quarterly 2017, Vol. 80, 1-19.
Thanks to the Internet and the related availability of “Big Data,” social interactions and their environmental context can now be studied experimentally. In this article, we discuss a methodology that we term the online field experiment to differentiate it from more traditional lab-based experimental designs. We explain how this experimental method can be used to capture theoretically relevant environmental conditions while also maximizing the researcher’s control over the treatment(s) of interest. We argue that this methodology is particularly well suited for social psychology because of its focus on social interactions and the factors that influence the nature and structure of these interactions. We provide one detailed example of an online field experiment used to investigate the impact of the sharing economy on trust behavior. We argue that we are fundamentally living in a new social world in which the Internet mediates a growing number of our social interactions. These highly prevalent forms of social interaction create opportunities for the development of new research designs that allow us to advance our theories of social interaction and social structure with new data sources.
“Risk Aversion and Engagement in the Sharing Economy.”
Santana, J.; Parigi, P. Games 2015, 6, 560-573.
The sharing economy is a new online community that has important implications for offline behavior. This study evaluates whether engagement in the sharing economy is associated with an actor’s aversion to risk. Using a web-based survey and a field experiment, we apply an adaptation of Holt and Laury’s (2002) risk lottery game to a representative sample of sharing economy participants. We find that frequency of activity in the sharing economy predicts risk aversion, but only in interaction with satisfaction. While greater satisfaction with sharing economy websites is associated with a decrease in risk aversion, greater frequency of usage is associated with greater risk aversion. This analysis shows the limitations of a static perspective on how risk attitudes relate to participation in the sharing economy.