Socialization in language learning: Topic modeling and bibliometric analysis

Abstract

This study identifies research status, trends, and topics within the field of socialization in language learning using a topic-based bibliometric methodology combining structural topic modeling, the Mann-Kendall trend test, and keyword analysis with bibliometric indicators. Based on relevant literature on socialization in language learning from the Web of Science database, we focused on answering questions such as “what technologies have been adopted to facilitate socialization in language learning?” and “what research topics have been investigated by scholars?” to understand where the research field has been and where it is going. Because traditional manual coding and systematic reviews are prone to error and coding inconsistency when evaluating limited literature data, we integrated rigorous machine learning algorithms and statistical tests, using a topic-based bibliometric methodology. Such a methodology is smarter because it allows time-honored bibliometrics to mine large volumes of literature data enabling a comprehensive understanding of diverse aspects of socialization in language learning. These include the top contributors (e.g., journals, subjects, countries/regions, and institutions), research topics and the dynamics of interest in topics. Results derived from our smart analysis provide insights into effective ways to advance smart computer-assisted language learning from a socialization perspective through scientific collaborations and the effective adoption of innovative technologies and analytical techniques.