The presence of occupational structure in online texts based on word embedding NLP models
This research addresses social stratification analysis for sociologists and computational social scientists, offering incremental insights by validating classical results and uncovering a previously undiscussed factor.
The study tackled the problem of analyzing occupational prestige by examining positions in semantic space from online texts using word embedding NLP models, and found fundamental similarity to traditional prestige scales while identifying a new factor related to power and organizational aspects.
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.