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.
Previous research on interaction behavior among organizations has typically aggregated those behaviors over time as a network of organizational relationships. This paper instead studies structural-temporal patterns in organizational exchange. Applying this lens to a community of Italian hospitals during 2003–7, the authors observe two mechanisms of interorganizational reciprocation: organizational embedding and resource dependence, and show how these two mechanisms operate on distinct time horizons and operate differently in competitive and non-competitive contexts. Results shed light on the evolution of generalized exchange or status hierarchies at the population level.
Kitts, James A., Lomi, Alessandro, Mascia, Daniele, Pallotti, Francesca, and Eric Quintane. “Investigating the Temporal Dynamics of Interorganizational Exchange: Patient Transfers Among Italian Hospitals.” American Journal of Sociology. 123(3): 850-910, 2017.
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.
Experimental and field research has demonstrated a pervasive tendency toward pairwise conformity among individuals connected by positive social ties, and work using formal models has shown that opinions on connected influence networks should thus converge toward uniformity. Observing that diversity persists even in small scale groups and organizations, we investigate two empirically grounded mechanisms of social differentiation that may account for this persistence: First, actors may dislike or disrespect peers who diverge too much from their own views, and may change their opinions or behaviors to distance themselves further from those negative referents. Second, when surrounded by similar others, actors may try to maintain a sufficient sense of uniqueness by exploring new opinions or behaviors. Using computational experiments, we demonstrate that these two mechanisms lead to different patterns of polarization, radicalization, and factionalism and also investigate the conditions under which integration occurs. In closing, we discuss the implications for cultural dynamics in organizations.
Mäs, Michael, Flache, Andreas, and James A. Kitts. “Cultural Integration and Differentiation in Groups and Organizations.” Perspectives on Culture and Agent-Based Simulations: Integrating Cultures, Springer, 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.
This study examines how information that may reduce uncertainty affects individuals’ trust in online exchange. Within an experimental marketplace, human subjects make purchase decisions with a series of vendors. Subjects receive information about vendors in the form of ratings of transaction security that vary as to the source of reputation information (interpersonal vs. institutional sources) and the content of information (rating of reliability vs. capability for engaging in secure transactions).
Anthony, Denise, Kitts, James, Masone, Christopher, and Sean W. Smith. “Internet Exchange and Forms of Trust.” In Trust and Technology in a Ubiquitous Modern Environment. Edited by Dominika Latusek and Andrea Gerbasi. IGI Global, 2010.
Trust plays an important role in both group cooperation and economic exchange. As new technologies emerge for communication and exchange, established mechanisms of trust are disrupted or distorted, which can lead to the breakdown of cooperation or to increasing fraud in exchange. This paper examines whether and how personal privacy information about members of distributed work groups influences individuals’ cooperation and privacy behavior in the group. Specifically, we examine whether people use others’ privacy settings as signals of trustworthiness that affect group cooperation. In addition, we examine how individual privacy preferences relate to trustworthy behavior. Understanding how people interact with others in online settings, in particular when they have limited information, has important implications for geographically distributed groups enabled through new information technologies. In addition, understanding how people might use information gleaned from technology usage, such as personal privacy settings, particularly in the absence of other information, has implications for understanding many potential situations that arise in pervasive computing environments.
Anthony, Denise, Kitts, James, Masone, Christopher, and Sean W. Smith. “Trust and Privacy in Distributed Work Groups.” In Social Computing and Behavioral Modeling. Edited by Dominika Latusek and Andrea Gerbasi. IGI Global, 2009.