Professor of Sociology, Founding Co-Director of the Computational Social Science Institute
This paper replicates and investigates recent findings of order effects in social network data collection, where later names on a roster receive fewer nominations. We model order effects as biases in nomination choices and demonstrate observational and experimental methods for assessing these biases and illuminating their mechanisms.
Liu, Shuyin, Nolin, David, and James A. Kitts. “Name Order Effects in Measuring Adolescent Social Networks Using Rosters” Social Networks. 76: 68-78, 2024.
This paper reviews two threads of research in Computational Social Science, one that studies face-to-face interaction using sensors or other electronic devices, and another that studies computer-mediated interaction via social media or telecommunication technology. This chapter extends recent efforts (Kitts, 2014; Kitts & Quintane, 2020; Lewis, 2022) to enrich the dialogue between the new CSS data sources and conventional social network concepts and theories.
Kitts, James A., Grogan, Helene, and Kevin Lewis. “Social Networks and Computational Social Science.” In McLevey, John, Carrington, Peter J., and John Scott (Eds.) Sage Handbook of Social Networks (Second Edition), 2023.
This study investigates the meaning of friendship for eight diverse cohorts of sixth graders, challenging ubiquitous assumptions that friendships represent liking and social interaction, friendships are directed, and friendships are equivalent to one another. Adolescents primarily construe friendship as relational norms, expectations for mutual behavior, along with mutual liking and interaction. Boys and girls weight these dimensions differently in defining friendship.
Kitts, James A. and Diego F. Leal. “What Is(n’t) a Friend? Dimensions of the Friendship Concept Among Adolescents.” Social Networks. 66: 161-170, 2021.
This paper reviews and discusses two revolutions in the science of social networks: In the first revolution, new telecommunications and sensor technologies allow researchers to collect data on interaction events with unprecedented volume and granularity. In the second revolution, innovations in statistical methods are uniquely fit to make sense of these streaming data, by explicitly modeling temporal interdependence of relational events. This paper interrogates the mapping between conventional social network theories and these new forms of streaming interaction data, developing empirical frontiers for the analysis of interaction event histories.
Kitts, James A. and Eric Quintane. “Rethinking Social Networks in the Era of Computational Social Science.” In Light, Ryan and James Moody (Eds.) Oxford Handbook of Social Network Analysis. pp 71-97. Oxford: Oxford University Press, 2020.
Researchers employ four versions of the social network concept: A social network is seen as a set of socially constructed role relations (e.g., friends, business partners), a set of interpersonal sentiments (e.g., liking, trust), a pattern of behavioral social interaction (e.g., conversations, citations), or an opportunity structure for exchange. Researchers conventionally assume these conceptualizations are interchangeable as social ties, but important discrepancies appear, especially for non-ties. Investigating the interplay across the four definitions is timely because emerging tools of computational social science — wearable sensors, logs of telecommunication, online exchange, or other interaction — now allow us to observe the fine-grained dynamics of interaction over time. Cutting-edge lenses allow us to move beyond reified notions of social ties (and non-ties) and instead directly observe and analyze the dynamic and structural interdependencies of social interaction behavior.
Kitts, James A. “Beyond Networks in Structural Theories of Exchange: Promises from Computational Social Science” Advances in Group Processes. Vol 31, 2014.
This paper presents new methods for inferring colocation and conversation networks from fine-grained behavioral data collected automatically using wearable sensors. We discuss how we can derive meaningful information about social interaction from audio data, while protecting the privacy of human subjects. We apply these methods in a study of face-to-face interactions among 24 students in a graduate school cohort during an academic year.
Wyatt, Danny, Choudhury, Tanzeem, Bilmes, Jeff, and James A. Kitts. “Inferring Colocation and Conversational Networks Using Privacy-Sensitive Audio” ACM Transactions on Intelligent Systems and Technology, 2(1), 2011.
We present an automated approach for studying fine-grained details of social interactions and relationships. Specifically, we analyze the conversational characteristics of a group of 24 individuals over a six-month period, explore the relationship between conversational dynamics and network position, and identify behavioral correlates of tie strengths within a network. The ability to study conversational dynamics and social networks over long time scales, and to investigate their interplay with rigor, objectivity, and transparency will complement the traditional methods for scientific inquiry into social dynamics.
Wyatt, Danny, Choudhury, Tanzeem, Bilmes, Jeff, and James A. Kitts. “Towards the Automated Social Analysis of Situated Speech Data“ Proceedings of the International Conference on Ubiquitous Computing, September 2008.