Machine Learning as a Model for Cultural Learning: Teaching an Algorithm What it Means to be Fat. (November 2022)

• Record Type: Journal Article
• Authors: Arseniev-Koehler, Alina; Foster, Jacob G.
• Other Names: Bonikowski Bart guest-editor.; Nelson Laura K. guest-editor.
• Is Part Of: Sociological methods & research. Volume 51:Number 4(2022)
• Page Start: 1484
• Page End: 1539
• Publication Date: 2022-11
• Subjects: word embeddings -- schema -- culture -- meaning -- cognition; Sociology -- Research -- Periodicals; Sociology -- Methodology -- Periodicals; 301.072
• DOI: 10.1177/00491241221122603
• Languages: English
• ISSNs: 0049-1241

Abstract

Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis – neural word embeddings – can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight "fill in the blanks" about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.