The Social Media of Migrant Workers: A Bibliometric Analysis
Bibliographic record
Abstract
Introduction: Social media significantly influences the socio-economic and cultural experiences of migrant workers worldwide. It serves as a tool for communication, labor advocacy, identity negotiation, and social support among migrant communities.Objective: This study aims to explore the scholarly research landscape on the relationship between social media and migration from 2015 to 2025. It seeks to identify publication trends, thematic focuses, collaboration networks, and key contributors in this field to understand how research has evolved and where it is headed.Method: A bibliometric and thematic analysis was conducted using data from the Scopus database, focusing on publications from 2015 to 2025. The analysis employed the Bibliometrix R package and VOSviewer to map out publication trends, author networks, institutional collaboration, and research themes.Results: The analysis reveals a steady growth in publications related to social media and migrant workers. China and India emerged as the leading contributors to this field. Notable institutions include the Stockholm International Water Institute and the University of Toronto. The top 10 authors each contributed two publications on relevant themes. Thematic analysis highlighted recurring focuses on migrant communication, advocacy, identity, and support. Research collaboration networks show increasing international cooperation, although gaps in interdisciplinary approaches remain.Conclusions: The study highlights both the opportunities and challenges that social media presents for migrant workers and calls for more interdisciplinary research and inclusive digital policies to support migrant populations effectively. Given current trends, the topic is expected to remain highly relevant in the coming years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.144 | 0.174 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".