Why do liberals drink lattes? How lifestyles tied to political views can be self-reinforcing among partisan groups

The increasing polarization of U.S. politics has seen the rise of partisan ‘echo chambers’ with little interaction between those at opposite poles. This division has broadened to include lifestyles, with liberals often characterized as ‘latte-drinking’ and conservatives as ‘gun enthusiasts’, for example. In new research, Daniel DellaPosta, Yongren Shi, and Michael Macy look at how political ideology becomes linked to people’s lifestyles. They show how demographic influences on opinions can be amplified by the self-reinforcing dynamics of peer group interactions

Roots of Trumpism: Homophily and Social Feedback in Donald Trump Support on Reddit

We study the emergence of support for Donald Trump in Reddit's political discussion. With almost 800k subscribers, "r/The_Donald" is one of the largest communities on Reddit, and one of the main hubs for Trump supporters. It was created in 2015, shortly after Donald Trump began his presidential campaign. By using only data from 2012, we predict the likelihood of being a supporter of Donald Trump in 2016, the year of the last US presidential elections. To characterize the behavior of Trump supporters, we draw from three different sociological hypotheses: homophily, social influence, and social feedback. We operationalize each hypothesis as a set of features for each user, and train classifiers to predict their participation in r/The_Donald. We find that homophily-based and social feedback-based features are the most predictive signals. Conversely, we do not observe a strong impact of social influence mechanisms. We also perform an introspection of the best-performing model to build a "persona" of the typical supporter of Donald Trump on Reddit. We find evidence that the most prominent traits include a predominance of masculine interests, a conservative and libertarian political leaning, and links with politically incorrect and conspiratorial content.Comment: 10 pages. Published at WebSci2

Pluralistic Collapse: The “Oil Spill” Model of Mass Opinion Polarization

Despite widespread feeling that public opinion in the United States has become dramatically polarized along political lines, empirical support for such a pattern is surprisingly elusive. Reporting little evidence of mass polarization, previous studies assume polarization is evidenced via the amplification of existing political alignments. This article considers a different pathway: polarization occurring via social, cultural, and political alignments coming to encompass an increasingly diverse array of opinions and attitudes. The study uses 44 years of data from the General Social Survey representing opinions and attitudes across a wide array of domains as elements in an evolving belief network. Analyses of this network produce evidence that mass polarization has increased via a process of belief consolidation, entailing the collapse of previously cross-cutting alignments, thus creating increasingly broad and encompassing clusters organized around cohesive packages of beliefs. Further, the increasing salience of political ideology and partisanship only partly explains this trend. The structure of U.S. opinion has shifted in ways suggesting troubling implications for proponents of political and social pluralism.</jats:p

Replication Data for: Pluralistic Collapse (ASR, 2020)

Replication data and code for "Pluralistic Collapse: The 'Oil Spill' Model of Mass Opinion Polarization." Code should be run in this order: Step 1 - Variable_Recodes.do (Stata; this can also be skipped if you just want to start with the R files and use the prepared GSS_Recoded.dta file) Step 2 - Create Pairs Step 3 - Gather Correlations Step 4 - Model Correlations Step 5 - Observed Networks Step 6 - Bootstrapping (parts1, 2, and 3 in that order) Step 7 - Plot Results Note that several of these steps are computationally intensive. The "parallel" package in R can be very useful for purposes of speeding up the computation. *Note*: In a previous version of this dataset, I forgot to include the Step 5 file which is used to generate the observed (non-bootstrapped) networks. This is now included, and the subsequent files have been relabeled

Wise Guys: Closure and Collaboration in the American Mafia

How do organizations obtain access to valued resources without diluting the loyalties and identities of their members? Network analysts suggest focusing on the boundary-spanning activities of "brokers" who bridge gaps in social structure. In many contexts, however, brokers are viewed with suspicion and distrust rather than rewarded for their diversity of interests. This dissertation examines organizations in which the theoretical deck is seemingly stacked against brokerage and toward parochialism: American-Italian mafia families. Through an institutional analysis of the mafia organization, I trace how ethnic and organizational closure led marginalized actors to seek alternative paths to enrichment beyond the family-controlled networks and industries. Using a historical network data set, I document a division of network labor in which a small number of brokers - often, surprisingly, ethnic outsiders and lower-status criminals - bridged otherwise disconnected islands of criminal activity. More than coordination among elite criminals, it was entrepreneurial action by marginal and excluded actors - outsiders operating largely beyond the control of mafia organizations themselves - that generated the integrated and highly connected mafia network. This dissertation accounts for a striking historical paradox by showing how it was possible for the American Mafia to appear for all intents and purposes to be a well-organized national conspiracy even as the individual groups involved remained organizationally and geographically separate from one another

Replication Data for: The Complexity of Associative Diffusion

This R code replicates Goldberg & Stein's (2018, ASR) agent-based model of associative diffusion