Visualizing the knowledge domain of multimodal discourse analysis 2009-2019: A bibliometric review

Authors

  • Chunlei Chen

    College of Foreign Languages and Cultures, Sichuan University

DOI:

https://doi.org/10.18063/fls.v2i1.1205

Abstract

Different from traditional discourse analysis, multimodal discourse analysis (MDA), a systematic analysis of different semiotic modes, utilizing language, images, sounds in a discourse, emphasizes the coordination of both dynamic and static semiotic resources. This study presents the status quo and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from the Web of Science Core Collection. Analysis techniques including a descriptive statistical method and a bibliometric method are used. The study quantitatively analyzes the publications in terms of general characteristics, geographical distribution, high-cited representatives, and topic discovery and distribution to illustrate the development and trend of MDA. The research findings are as follows: (1) In the past 10 years or so, international MDA research has presented a significant growth trend, with flourishing research output, interest and diversification of presented subjects; (2) New topics are constantly emerging, with research topics mainly focusing on the development of visual grammar, gesture, digital technologies, conference presentations, metonymy and metaphor, etc.; (3) Research focuses mainly on multimodality, semiotics, conversation analysis, critical discourse analysis etc.; (4) The article also listed a series of important and highly influential literature, countries, journals and authors on MDA during different periods. It is hoped that this paper can provide a reference for the further study of MDA.

Keywords:

multimodal discourse analysis; bibliometric; Citespace; review

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How to Cite

Chen, C. (2020). Visualizing the knowledge domain of multimodal discourse analysis 2009-2019: A bibliometric review. Forum for Linguistic Studies, 2(1). https://doi.org/10.18063/fls.v2i1.1205

Issue

Vol. 2 , Iss. 1 (October 2020)

Article Type

Review

License

Copyright © 2020 Chunlei Chen

Creative Commons License

This is an open access article under the Creative Commons Attribution 4.0 International License.