A Bibliometric Survey on Multimodal Discourse Analysis (2015–2024): Looking Behind to Look Ahead
Bibliographic record
Abstract
Different from traditional discourse analysis, multimodal discourse analysis (MDA) investigates multiple semiotic modes such as language, images, and sounds. It emphasizes the coordination between dynamic and static semiotic resources in discourse. This study conducts a quantitative bibliometric analysis to explore the research trends, hotspots, and intellectual structure of MDA, aiming to provide a comprehensive overview of its development and evolution. Using VOSviewer 1.6.20 software, this study assessed existing publications, particularly those conducted between 2015 and 2024, presenting the status quo and development trend of the research field through an objective, systematic, and comprehensive review of relevant publications available from Scopus. A total of 1562 articles on MDA were identified, with contributions from 2081 institutions across 81 countries and regions. The research findings are as follows: (1) in the past 10 years, international MDA research has presented a significant growth trend, with flourishing research output, interest and diversification of presented subjects; (2) emerging research trends in MDA highlight a growing focus on digital discourse, crisis communication, and global sociopolitical contexts.; (3) critical and ideological analyses are gaining prominence, alongside increasing interest in multimodal literacy within educational and ESP settings etc.; (4) the field is also expanding into interdisciplinary and applied domains, emphasizing real-world applications across media, education, and public policy. This study provides valuable insights into the development of MDA and serves as a reference for future research in the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".