Computational Social Science and Sociology

Abstract

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. Expected final online publication date for the Annual Review of Sociology, Volume 46 is July 30, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Charter School Authorizations as Disputes: How School Board Members Justify Their Votes in a Neoliberal Context1

“…There are drawbacks to a single case study, however, and future work will need to determine generalizability using comparative approaches. Examining multiple school boards over a long period using manual coding may prove prohibitive but future research could employ automatic text analysis like topic models and word embeddings (Edelmann et al, 2020). Additionally, charter schools are not the only morally complex issue faced by educators.…”

Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change

“…Clustering text documents using K-means has a long history [47] and assumes that every document belongs to one of K latent classes (or types); by analyzing the word frequencies of text with the same latent class assignment, it is often possible to understand the meaning of clusters through their most frequent words. In a related vein, others use unsupervised topic modelling methods such as Latent Dirichlet Allocation [48], increasingly common in social sciences [49,50], to examine semantic trends in urban spaces (e.g. [51]).…”

Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change

“…Clustering text documents using K-means has a long history [47] and assumes that every document belongs to one of K latent classes (or types); by analyzing the word frequencies of text with the same latent class assignment, it is often possible to understand the meaning of clusters through their most frequent words. In a related vein, others use unsupervised topic modelling methods such as Latent Dirichlet Allocation [48], increasingly common in social sciences [49,50], to examine semantic trends in urban spaces (e.g. [51]).…”