Assembling Topic Models: Material Political Economy and the Genealogy of an Algorithm.

Abstract

Natural Language Processing (NLP) technologies-ranging from topic models to today's large language models like GPT-have rapidly entered the social sciences, reshaping methodological practice. Yet researchers often overlook the stark political-economic contrasts between academia and the AI research-industry symbiosis. Identical algorithms, once embedded in different institutional settings, acquire different meanings and standards of evaluation. This paper shows the divergence by examining topic modeling, a classical NLP technique in computational social science. Social scientists grapple with the instability of applying topic models to the same corpus, whereas in the AI industry such variability matters little, given different evaluative priorities. Through a comparative analysis of topic modeling's trajectory across AI and social science, I show how organizational contexts and goals shape the development of the same algorithms, and why framing instability as a purely technical issue is problematic in the social sciences. The findings reveal that algorithms are not simply technical tools but products of material political-economic regimes. Recognizing this, I argue that STS scholars have a vital role to play in computational social science: not only by critically examining and developing methods, but also by interrogating the material-political-economic regimes in which algorithms are enacted, and by working toward more just alternatives.