Devin J. Cornell

Sociology PhD Candidate at Duke University

I use machine learning and network analysis methods to study the emergence and transformation of meanings as they appear in newspapers, social media, and other public forums.

My past projects have included topics such as moral framing in news articles, ideological foundations of political cleavages, intra-party influence in party discourses, political influence on chat platforms, and changes in gender stereotypes across the 20th century. See my CV (to the left) for more information.

Recent Work

Discursive Fields and Intra-party Influence in Colombian Politics

MA Thesis Committee: John W. Mohr, Maria S. Charles, Verta Taylor

Thesis on UC Santa Barbara eScholarship

When are politicians influential in shifting party discourse? This study explores how same-party politicians influence one another, and how this influence leads to changes to a party's larger discourse. I suggest that the extent to which politicians are able to influence other party politicians depends on how their messages situate them within the party’s discursive field. I further suggest that certain messages are particularly influential when distinctive within a given time period. To assess this effect, I use a case study of just under 1 million Tweets from politicians in the Colombian political party Centro Democrático from 2015-2017. I use topic modeling and network analysis to measure influence within a dynamic discursive field, and a genetic learning algorithm to identify types of messages, as topics, which constitute the field under which we observe the strongest linkage between field position and influence. I find that politicians are influential when posting about current events and when creating symbolic distinctions which are central to the party ideology - in the case of Centro Democrático, distinctions between the concept of peace itself and the peace process developing in Colombia. These results suggest that the discursive field can be a powerful tool for analysis of influence and political discourse.

School, Studying, and Smarts: Gender Stereotypes and Education Across 80 Years of American Print Media, 1930-2009

Andrei Boutyline, Alina Arseniev-Koehler, Devin Cornell

Working Paper on SocArXiv

Gender stereotypes have important consequences for boys’ and girls’ academic outcomes. In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930-2009) to examine how these stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This transformation presents a rare opportunity to observe how stereotypes change alongside the reversal of an important pattern of stratification. We track six stereotypes that prior work has linked to academic outcomes. Our results suggest that stereotypes of socio-behavioral skills and problem behaviors—attributes closely tied to the core stereotypical distinction between women as communal and men as agentic—remained unchanged. The other four stereotypes, however, became increasingly gender-polarized: as women’s academic attainment increased, school and studying gained increasingly feminine associations, whereas both intelligence and unintelligence gained increasingly masculine ones. Unexpectedly, we observe that trends in the gender associations of intelligence and studying are near-perfect mirror opposites, suggesting that they may be connected. Overall, the changes we observe appear consistent with contemporary theoretical accounts of the gender system that argue that it persists partly because surface stereotypes shift to reinterpret social change in terms of a durable hierarchical distinction between men and women.



Python package for parsing, storing, and accessing text documents and models for large scale text analysis.


GitHub Project