BIBLIOMETRIC ANALYSIS OF SCIENTIFIC PUBLICATIONS ON THE KEYWORD “CORPUS LINGUISTICS”: BASED ON SCOPUS DATA (2015–2025)

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.32523/2616-678x-2026-155-2-119-136
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This study aims to identify development trends, structural characteristics, and major research directions in scientific publications in the field of corpus linguistics. The purpose of the research is to determine the scientific productivity of the field, publication dynamics, leading authors and countries, patterns of international scientific collaboration, as well as dominant thematic trends by conducting a bibliometric analysis of publications on corpus linguistics published between 2015 and 2025 based on the Scopus database. Bibliometric analysis was used as the main research method. Data were retrieved from the Scopus database using the keyword “corpus linguistics,” resulting in 23,073 documents; after applying a time filter (2015–2025), 16,475 documents were downloaded, and following data cleaning with the Bibliometrix software, 16,402 scientific documents were included in the analysis. Bibliographic data were processed in the RStudio programming environment using the Bibliometrix package and the Biblioshiny interface. The analysis examined the annual dynamics of publications, author and country-level collaboration, the most frequently used keywords, and thematic networks. The results indicate rapid growth in corpus linguistics publications, expanding international collaboration, and a shift toward computational approaches. The study systematizes current trends and provides a foundation for future research.