The presence of occupational structure in online texts based on word embedding NLP models

TL;DR

This paper investigates the semantic positioning of occupations in large textual datasets using word embedding models to compare with traditional social stratification theories, revealing both similarities and new insights.

Contribution

It demonstrates that occupational structures derived from NLP models align with classical social stratification results and highlights the role of organizational power as a new factor.

Findings

Occupational positions in text reflect social prestige.

Semantic space analysis reproduces traditional social hierarchy.

Power and organizational aspects are significant in occupational stratification.

Abstract

Research on social stratification is closely linked to analysing the prestige associated with different occupations. This research focuses on the positions of occupations in the semantic space represented by large amounts of textual data. The results are compared to standard results in social stratification to see whether the classical results are reproduced and if additional insights can be gained into the social positions of occupations. The paper gives an affirmative answer to both questions. The results show fundamental similarity of the occupational structure obtained from text analysis to the structure described by prestige and social distance scales. While our research reinforces many theories and empirical findings of the traditional body of literature on social stratification and, in particular, occupational hierarchy, it pointed to the importance of a factor not discussed in the main line of stratification literature so far: the power and organizational aspect.