Exploring Data Scientists in Scientific Literature: LDA Topic Modeling on the Semantic Scholar Database
Łukasz Iwasiński
Afiliacja: , Polska
Len Krawczyk
Afiliacja: Uniwersytet Warszawski, Wydział Socjologii, Polska
Mateusz Szymański
Abstrakt
Purpose/Thesis: This paper explores the representation of data scientists in scientific literature. It aims to answer the following quiestions: How has the number of publications on data scientists evolved over time? How are papers regarding data scientists distributed over different fields of study? In what context data scientists are represented in scientific literature?
Approach/Methods: Authors used Latent Dirichlet Allocation (LDA) topic modeling to the resources available within the Semantic Scholar API.
Results and conclusions: There is an increase in the number of publications on data scientists since 2008. A robust connection between data scientists and information technology as well as biomedical research was found. Little literature discusses data scientists in sociocultural context.
Originality/Value:
To our knowledge no studies heve been devoted to the representation of data scientists in scientific literature. The research may contribute to conceptualization of this notion.
Słowa kluczowe
Data Science; Text Mining; Latent Dirichlet Allocation; Topic Modeling; Semantic Scholar
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Afiliacja: , Polska
Afiliacja: Uniwersytet Warszawski, Wydział Socjologii, Polska
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CC BY-NC-ND 4.0 Uznanie autorstwa - Użycie niekomercyjne - Bez utworów zależnych 4.0 Międzynarodowe
