Tracking the spread of ideas in academia with text analysis

Claire Maiers and Nicholas Napoli aimed to uncover the patterned ways by which ideas move through social systems. They examined the ways in which concepts and methods diffuse, endure and decline within academic fields. 

Using the JSTOR database, which contains more than 4.5 million academic articles published between 1900 and 2015, Maiers and Napoli tracked the prevalence and movement of concepts and methods through a textual analysis of articles. 

They explored two different methods for tracking concepts and ideas within this corpus:

  1. A supervised approach to identify words and concepts of interest
  2. An unsupervised topic modeling approach

Maiers and Napoli developed a typology of the patterns by which concepts and methods move through individual disciplines and spread across disciplines.

Claire Maiers is a PhD student in the Department of Sociology. Her dissertation will examine data science as a mode of knowledge production.

Nicholas Napoli is a PhD student in the Department of Systems and Information Engineering. His expertise is in signal processing, statistical learning and pattern recognition.

Completed in:
2016
Researchers:

Claire Maiers and Nicholas Napoli