Unveiling Research Landscape in Social Media and Corporate Social Responsibility: A Bibliometric Analysis
Bibliographic record
Abstract
Corporate Social Responsibility (CSR) has evolved beyond traditional philanthropy into a strategic imperative for organisations, and social media now serves as a vital channel for promoting, implementing, and evaluating CSR initiatives. This intersection has transformed how businesses engage with stakeholders, respond to social expectations, and build reputational capital. There are three research questions in our study: (1) What is the demographic information of the social media and CSR?; (2) What are the dominant themes?; and (3) which countries are leading the conversation, and how are they collaborating? Our study presents a bibliometric analysis of academic research at the intersection of corporate social responsibility (CSR) and social media, covering the period from 2009 to 2025, based on the Scopus database. Prominent themes include communication and consumer behaviour, empirical studies and methodology, CSR communication strategies, stakeholder and legitimacy, and reputation and activism. Key contributors, such as Cheng, Y., Chen, Y.R.R., and Hung-Baeseke, C.J.F., while journals like Sustainability and The Journal of Business Ethics, were among the five most prolific journals in the field of social media and Corporate Social Responsibility (CSR). Geographically, the United States was the dominant country in the publication, followed by other countries like China, Spain, the United Kingdom, Australia, Italy, and Canada. The co-authorship and co-citation networks highlight the collaborative and interdisciplinary nature of CSR-social media research. Our study identified five (5) key themes occurrences in published CSR and social media research. This paper includes a discussion of the limitations encountered as well as further suggestions for future research. Keywords: CSR, social media, Scopus, bibliometrics analysis, corporate social responsibility.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.057 | 0.109 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".