It is well-known that the more educated people are, the more liberal views they tend to express. However, it is unclear whether this is due to college attendance itself or because those who go to college differ from those who do not in ways (directly or indirectly) related to their later political identification. In this paper, we therefore attempt to estimate the effect of college on political identification net of people's tendencies to select into college using an inverse probability of treatment weighting approach. Based on data from the General Social Survey, we analyze how this effect has changed over time and whether college affects the political identification of women in the same ways as that of men. We find evidence consistent with the argument that college attendance politicizes both men and women. Moreover, we show that not only the general, but also the gender specific effects change markedly across the decades. This raises questions about the different mechanisms at play in how college mobilizes men and women politically.

Figure: Difference in proportion of non-moderates between college goers and non-goers by gender.

The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; (f) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.

Figure: Number of computational social science publications by year-2003-2016-across five scholarly disciplines.

Scholars of social influence can benefit from attending to symbolic boundaries. A common and influential way to understand symbolic boundaries is as widely shared understandings of what types of behaviors, tastes, and opinions are appropriate for different kinds of people. Scholars following this understanding have mostly focused on how people judge others and how symbolic boundaries align with and thus reproduce social differences. Although this work has been impressive, I argue that it might miss important ways in which symbolic boundaries become effective in everyday social life. I therefore develop an understanding of how symbolic boundaries affect people’s ideas and decisions about themselves and their own behavior. Based on this, I argue that focusing on boundary violations—that is, what happens if people express opinions or enact behavior that contravenes what is considered (in)appropriate for people like them—might offer an important way to understand how symbolic boundaries initiate and shape cultural and social change. Using data from Add Health, I demonstrate the utility of this line of argument and show that boundary violations play an important role in channeling social influence. Conservative/Evangelical Protestants and to a lesser degree Catholics, but not Mainline Protestants are highly influenced by the drinking of co-religionists. I consider the implications for cultural sociology.

This paper introduces the Culturally Meaningful Networks (CMN) approach. Following a pragmatist perspective of social mechanisms more broadly, it develops and demonstrates an approach to understanding networks that incorporates both structure and meaning and leverages time to understanding how these aspects influence each other. I apply this approach to investigate a longstanding puzzle about why some of those who leave military service for civilian life fare well, and others badly. In a mixed-methods analysis, I follow a sample of individuals moving through the transition from military to civilian life in the contemporary UK. I find that the higher the proportion of alters with a military background in leavers’ networks before discharge the worse they fare after discharge. The CMN approach helped me locate a specific structural embedding that explains the presence or absence of durable cultural frames that set the context for the actual experience of the transition, and cause problems during it. Attention to the temporal unfolding of network structure and social meaning is essential to bringing out this finding. By re-embedding networks within people’s experiences over time, the CMN approach helps grasp the distinctions by which leavers understand their interactions. I conclude by arguing that the CMN approach has implications for network sociology and cultural sociology that go beyond this substantive case.

Formal efforts to advance culture as an analytic concept can benefit from attending to the human disposition to categorize. I demonstrate this with regard to the concept of symbolic boundaries, which I conceptualize as dual classifications that describe what kind of behavior or opinion is appropriate for different kinds of people and put forward a corresponding (mathematical) formalization. Drawing on insights from cognitive science about categorization, I develop an understanding of “boundary breaching,” that is, what happens when these dual classifications are violated. This allows me to derive a model of network influence that accounts for how people evaluate behavior in others based on cultural classifications. I demonstrate how this model can be implemented empirically using data from the National Study of Youth and Religion. By drawing on our cognitive disposition to categorize, symbolic boundaries mediate structural effects of social influence.

$$Y = (GG′•A)Y•(–GSy) + AY + BX + U$$
Model of social influence accounting for the effect of symbolic boundary \(S\) with regard to behavior \(y\); equilibrium condition shown. For details, see the paper.

Over the last two decades, the formal study of culture has grown into one of the most exciting, systematic, and dynamic sub-fields in sociology. In this introductory essay, we take stock of recent developments in this field. We highlight four emerging themes: (1) the maturation of the field that has occurred over the last two decades, (2) the rise and formalization of the “cultural matrix” approach to studying culture, (3) the development of various efforts to advance a more formal theory of culture, and (4) the proliferation of Big Data and the development of new kinds of quantitative and computational approaches to the study of culture, including the emergence of a new area focused on “computational hermeneutics.” We conclude by discussing future opportunities, challenges, and questions in formalizing culture.

I ask what shapes scientists’ positions on policy debates which have the potential to re-define the future of society. I examine the formal public disagreement amongst high-status scientists, including Nobel laureates, concerning gain-of-function research—that is, research in which an entity such as a virus is manipulated to gain a new property such as transmissibility among species other than their natural hosts—that involves pathogens capable of triggering serious pandemics. Within this debate, two opposing factions emerge, both rooting their positions in competing claims to public-safety, providing a unique opportunity to understand what drives scientists’ policy positions when empirical resolutions are lacking. After scraping signatures from the petitions of both groups, I identify 19,257 academic papers belonging to these scientists. Then, using network analytical techniques, I identify their collaborative networks and position in the social structure of the debate. Employing generative probabilistic topic modeling, I derive the core themes that these scientists have worked on and approximate their location in the overall intellectual space of the debate. I find that participants’ policy positions are differentially predicted by their collaborators and their own research focus: peer behavior best predicts those supporting bans of gain-of-function research while research specialty best predicts those supporting restarting active research. The differential foundations for positions suggests that scientists’ policy positions are likely informed more by peers and interests than by empirical evidence.

Figure: Collaboration and position on gain-of-function research. (A) Scientists’ collaboration network. Nodes are petition signers, and edges are collaborations; layout via Fruchterman–Reingold, which tends to place scientists near collaborators; node size is proportionate to the total number of collaborators; n = 378. (B) Predicted probabilities of signing the SFS petition by number of collaborators who signed either the SFS (green) or CWG (orange) petition. Probabilities are based on logistic regression models (SI Appendix, Table S3); the shaded area represents the 95% CI for the “all controls” model, which adjusts for specialization, publication volume, and demographic characteristics; n = 378.

Figure: Correspondence between research topics and position on gain-of-function research. Map of the largest component of scientists’ paper-to-paper coterm network: edges link papers (n = 19,257) weighted by their cosine similarity (see the SI Appendix for details); layout is via Fruchterman–Reingold, which places similar papers near one another; layout positions are constant in both A and B. (A) Edges colored by papers’ highest loading topic (eight colors, corresponding labels positioned near the center of topic clusters). (B) Edges colored by the authors’ camp (green, SFS; orange, CWG).

While scholars have long argued for the importance of cultural forms in determining the nature of social relationships, expositions of related concepts and mechanisms have remained vague and subject to multiple interpretations. Addressing this shortcoming is the challenge that I take up in this paper. I compare two prominent understandings of culture to explicate and test the different mechanisms they imply for how cultural tastes affect social relationships. I draw upon a longitudinal network dataset that traces relationships among college students over the course of an academic year and apply an actor-oriented network modeling technique to explain the development of relationships over time. I show that an understanding of cultural tastes as “resources” or “skills” (A. Swidler) is incomplete and that if we are to adequately account for the development of social relationships then it is also necessary to approach culture as a “system of distinctions" (P. Bourdieu).


Figure: Network among students across time. Ties defined as having socialized at least once since the start of the current academic term; network limited to students with complete information for all terms; node size as a function of degree.

In this project, I use 29,513 bibliographic records from Web of Science to map and analyze the emergence of a new subfield in ecology called “Network Ecology" that represented more than 5% of the ecological publications in 2012. Based on author listings, I characterize the structure of scientific collaboration in this field by using network analysis. Based on co-citation networks, I then map the topic structure of this field, thereby allowing us to understand the social and thematic landscapes of this new sub-discipline as it emerges over time.

Figure: Contour plot of co-citation network. Based on bibliographic records pooled across all years; edges above a 35% minimum similarity considered; network is limited to the largest component, representing 73% of the papers; regions labeled with the most common terms found in the clusters and font size corresponds to term frequency.