Analyzing Bibliometric Trends in the Social Internet of Things: A Review and Future Perspectives
Bibliographic record
Abstract
The study aims to fill the gap in the bibliometric analysis of the Social Internet of Things (SIoT) discourse, focusing on recurring patterns, exploring uncharted study domains, and proposing future directions in the developing interdisciplinary realm.The analysis used quantitative data from the Scopus database from 2012 to 2023, covering architecture, trust management, service composition, network navigability, and integration with upcoming technologies.The study revealed a 29.14% yearly expansion rate in SIoT research, demonstrating a dynamic and cooperative research environment.Keyword clusters included social interactions, multidisciplinary perspectives, network architecture, cybersecurity, trust administration, and social networking integration.The bibliometric study provides valuable insights for researchers, practitioners, and policymakers, enabling them to navigate the ever-changing Smart Internet of Things landscape and promote interdisciplinary collaboration for further advancements.Future studies should include qualitative assessments, examine societal implications, and explore future technologies within the Internet of Things (IoT) framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.056 | 0.081 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".