Cooperation and trust in extra-legal contexts

I explore how agents sustain mutually beneficial cooperative relations in adverse contexts. Illegal online marketplaces (cryptomarkets) present a unique context for studying the emergence of cooperation in a context where legal controls are absent, social actors are highly anonymous, and the overall levels of trust are low. With collaborators, I study how socio-technical solutions (such as reputation systems) and informal mechanisms of social control (such as gossip) allow individuals to establish trade in such adverse extra-legal environments.

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The Moral Embeddedness of Illegal Markets

With Wojtek Przepiorka, Under Review.

In this work, we explore how reputation-based online markets have shifted the role that psychological mechanisms play in promoting mutually cooperative market exchange from the stage of exchange to the stage of sharing information about other traders in the market. We use text mining to infer reasons traders had for sharing reputation information about other traders in illegal online marketplaces and zoom in on the essential tole of moral norms in solving the second-order cooperation problem in large-scale anonymous internet marketplaces.

The Importance of Gossip in the Presence of Formalized Reputation Measures in Online Markets

With Wojtek Przepiorka and Vincent Buskens, In preparation.

In complex societies with increasing role specialization social control moves from the interpersonal to impersonal by being delegated to specially chosen agents and formalized institutions. The case of online marketplaces where hundreds of thousands geographically distant anonymous traders engage in transactions showcases the success of socio-technical solutions in supporting large-scale cooperation. In these markets, specially delegated administrators, buyer protection technologies, and formalized reputation systems enforce sanctions against untrustworthy traders. Yet, it remains unclear whether informal information sharing through gossip still plays an important role in enforcing (informal) social control in such technologically advanced marketplaces. In this work, we explore the interaction of gossip as a means of informal social control and more formalized socio-technical control systems existing in online markets.

Norm emergence and change

I am interested in how norms emerge to promote or block sucesfull cooperation in societies. In a project with collaborators, I use agent-based models to explore how norms that signal group belonging emerge to solve cooperation problems in contexts where multiple groups with conflicting interests encounter each other. In such contexts, signalling one’s group behaviour can help establish parochial cooperation and benefit exploited minority groups. I am further interested in understanding conditions under which such signalling norms become inefficient and damage the well-being of the groups that enforce them.

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Signals of Belonging: Emergence of Signalling Norms as Facilitators of Parochial Cooperation

With Milena Tsvetkova, Wojtek Przepiorka, and Vincent Buskens, In preparation.

Mechanisms of social control at times reinforce norms that appear individually, or even collectively, harmful or “wasteful” – such as mutilation practices or extensive body tattoos. This project explores the suggestion that such norms emerge as outcomes of signalling games and promote parochial cooperation in contexts where different groups have conflicting interests. To understand when signalling norms emerge as solutions to the trust problem arising in such environments, we build an agent-based model based on the trust game with signalling: in this game, signalling can help individuals reveal their group membership to others they are encountering – and thus enforce and sustain parochial cooperation.

Networks, culture, innovation and inequality

I am intrigued by how the structure of social networks affects outcomes in different social groups and communities. On the one hand, I am interested in how social network structure and cultural integration affect the capacity of (online) communities to develop shared culture and innovate. On the other hand, I am also intrigued by how numerical properties of different social groups engaging ininteractions with each other can lead to structural inequalities.

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“I Heard it on the (Silk)Road”: Structural and Cultural Pathways to Novelty Introduction in an Online Community

With Damiano Morando, In preparation.

Online communities play a crucial role in generating and spreading innovation which affects both the online and offline world. Yet, we still know relatively little about the members who introduce novelty into the community - innovators. We explore the structural and cultural embeddedness of members who introduce linguistic and knowledge-based innovations into a large online forum. First, we find that the effect of being structurally embedded in the community is moderated by the content one is exposed to. That is, less embedded individuals (i.e., brokers) benefit from access to diverse content in the online community;while embedded individuals benefit from seeing similar content.Second, we show that being culturally embedded in the community has an inverse U shape effect over the likelihood introducing an innovation. We find evidence for the tension between the norm-following required for meaningful innovations and the norm-breaking that pushes one to “think outside of the box”.

Minority group size moderates inequity-reducing strategies in homophilic networks

With Sam Zhang and Travis Holmes, In preparation.

Minorities are often disadvantaged in social networks, and their disadvantage can arise in the presence of two ubiquitous features of social networks: homophily and preferential attachment. This disadvantage often translates to less visibility and lower access to valuable social capital of minority group members. In this paper, we use the directed network model with preferential attachment and homophily (DPAH) to evaluate how minority groups fare in the presence of additional groups that could bridge the majority/minority gap, such as majority group members who support the minority group (allies), and minority group members that are incorporated into the majority group. Our results show that the marginal benefit of majority group members becoming allies increases with minority group size, and larger minority groups reach equity with a smaller proportion of incorporated members. These results suggest that interventions on structural inequities on networks can depend sensitively on the relative sizes of the groups involved. Our models also reveal the increasing difficulty of a minority group achieving parity as the group shrinks.

Text mining for sociological research

I am interested in better understanding how meaning can be extracted from large amounts of textual data and used in sociological theory building and testing. I explore how automatic text analysis can be used by social scientist and explore the benefits and limitations of working with big data sets and computational methods for text analysis.

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Text mining for social science – The state and the future of computational text analysis in sociology

Article published in Social Science Research available here.

The emergence of big data and computational tools has introduced new possibilities for using large-scale textual sources in sociological research. Recent work in sociology of culture, science, and economic sociology has shown how computational text analysis can be used in theory building and testing. This review starts with an introduction of the history of computer-assisted text analysis in sociology and then proceeds to discuss five families of computational methods used in contemporary research. Using exemplary studies, it shows how dictionary methods, semantic and network analysis tools, language models, unsupervised, and supervised machine learning can assist sociologists with different analytical tasks. After presenting recent methodological developments, this review summarizes several important implications of using large datasets and computational methods to infer complex meaning in texts. Finally, it calls researchers from different methodological traditions to adopt text mining tools while remaining mindful of lessons learned from working with conventional data and methods.

Text mining individual states in short texts

With Wojtek Przepiorka, Under Review. Preprint available here.

Sociologists have successfully used text mining to investigate discourse using news articles, official documents, and other sources. Yet, the potential of exploring millions of short texts generated spontaneously by individuals in online environments has remained untapped within the field. To fill this gap, we show how such texts can inform sociologists about individual internal states such as norms, motives, and stances, which thus far have been mainly elicited using surveys. We assess the performance of 581 variations of three text mining approaches–dictionary methods, supervised, and unsupervised machine learning–against the benchmark of texts coded by humans for complex schemes capturing individuals’ internal states. Our analysis includes coding feedback texts from an online market for motives for leaving feedback (N = 2,000) and tweet texts for moral values expressed in text (N = 3,832). We describe challenges arising with these different approaches and provide best-practice advice for future applications.