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.
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.
Centralized sanctions (selective incentives) and informal norms have been advanced as distinct solutions to collective action problems. This article investigates their interaction, modeling the emergence of norms in the presence of incentives to contribute to collective goods. Computational experiments show how collective action depends on a three-way interaction among the value of incentives, rivalness of incentives (ranging from independence to zero-sum competition), and group cohesiveness (effectiveness of peer influence). This investigation shows a broad range of conditions in which social norms promote the collective good, and thus peer influence complements a centralized regime of selective incentives. It also shows conditions in which the two systems clash because incentives lead to antisocial norms that discourage contributions to collective goods.
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Kitts, James A. ” Collective Action, Rival Incentives, and the Emergence of Antisocial Norms.” American Sociological Review, 71(2): 235-259, April 2006.
This paper explores the coevolution of social networks and behavioral norms. Previous research has investigated the long-term behavior of feedback systems of attraction and influence, particularly the tendency toward homogenization in arbitrary cultural fields. This paper extends those models by allowing that norms diffuse not only by simple contagion but through intentional sanctioning behavior among peers. Further, the model allows for negative relations, where actors differentiate themselves from enemies while seeking to align themselves with friends. Sociometric maps reveal nontrivial system dynamics – structural bifurcation, discrimination between factions, and cycles of deviance and solidarity – emerging from a few elementary agent-level assumptions.
Kitts, James A. “Social Influence and the Emergence of Norms Amid Ties of Amity and Enmity.” Simulation Modelling Practice and Theory, 14(3): 407-422, March 2006.
Why do populations often self-organize into antagonistic groups even in the absence of competition over scarce resources? We look for answers by exploring the dynamics of influence and attraction between computational agents. Our model is an extension of Hopfield’s attractor network. Agents are attracted to others with similar states (the principle of homophily) and are also influenced by others, as conditioned by the strength and valence of the social tie. Negative valence implies xenophobia (instead of homophily) and differentiation (instead of imitation). Consistent with earlier work on structural balance, we find that networks can self-organize into two antagonistic factions, without the knowledge or intent of the agents. We model this tendency as a function of network size, the number of potentially contentious issues, and agents’ openness and flexibility toward alternative positions. Although we find that polarization into two antagonistic groups is a unique global attractor, we investigate the conditions under which global uniformity or pluralistic alignments may also be equilibria.
Macy, Michael W., Kitts, James A., Flache, Andreas, and Steve Benard. “Polarization in Dynamic Networks: A Hopfield Model of Emergent Structure. Dynamic Social Network Modeling and Analysis, National Academies Press, 162-173, 2003.
This paper examines systematic biases in group members’ inferences about collective support for group norms. While theories of “looking glass perception” suggest a tendency to project our own preferences onto others, this paper shows that observed biases may simply reflect flows of information through social networks. Members conceal their counter-normative behavior and divulge it disproportionately within confidence relations. This predicts structured inference, where members’ inferences depend on their social ties, and also pluralistic ignorance, where members generally overestimate collective support for existing norms. These predictions are evaluated in a field study of perceived normative consensus in five vegetarian housing cooperatives. Results fail to support the intrinsic bias argument, but demonstrate these forms of information bias. Also, by locating this structural effect only in large groups that facilitate private conversation, findings highlight the mechanism of selective disclosure. This source of normative inertia may account for the puzzling stability of broadly unpopular norms in groups.
Kitts, James A. “Egocentric Bias or Information Management? Selective Disclosure and the Social Roots of Norm Misperception.” Social Psychology Quarterly, 66(3): 222-237, September 2003.
Recent research has focused on the role of social networks in facilitating individuals’ participation in protest and social movement organizations. This paper elaborates three currents of microstructural explanation, based on information, identity, and exchange. In evaluating these perspectives, the paper compares their robustness to multivalence, the tendency for social ties to inhibit as well as promote participation. Considering two dimensions of multivalence – the value of the social tie and the direction of social pressure – this paper discusses problems of measurement and interpretation in network analysis of movement participation.
Kitts, James A. “Mobilizing in Black Boxes: Social Networks and Social Movement Organizations”
Mobilization: An International Journal, 5(2): 241-257, October 2000.
This paper explores the role of social networks in channeling individuals’ involvement in local activism. A case study of a grassroots environmental group examines variation in members’ levels of involvement, using three levels of explanation: individual attributes, strong and weak ties between members, and memberships in other organizations. After demonstrating that high- and low-level members are very similar in personal attributes, it focuses on social ties and organizational affiliations. As expected, the data suggest that an individual’s level of involvement is increased by strong ties to other members, structural similarity to other high-level members, and fewer ties to non-members. Extramovement organizational affiliations are often assumed to diminish actors’ structural availability, though empirical research in differential recruitment has generally revealed a positive effect on participation in social movements. This study addresses a microstructural explanation for the variation between competition and mutualism in a local multiorganizational field, as it shows how organizational goals condition the effect of outside affiliations on level of participation.
Kitts, James A. “Not in Our Backyard: Solidarity, Social Networks, and the Ecology of Environmental Mobilization.” Sociological Inquiry, 69(4): 551-574, Fall 1999.
Kitts, James A., Macy, Michael W., and Andreas Flache. “Structural Learning: Attraction and Conformity in Task-Oriented Groups.” Computational and Mathematical Organization Theory, 5:129-145, 1999.