Research Trends and Thematic Insights from the Most Cited Cybernetics Studies in the Last Ten Years Using Text Mining and Bibliometric Analysis
Bibliographic record
Abstract
According to the Web of Science (WoS) bibliometric data source, a total of 201,913 documents have been produced in all years, and 93,836 in the last decade, in the field of Computer Science and Cybernetics. 29,113 relevant documents are classified as research articles. In this study, the 1,000 most cited research articles from the past decade in the Computer Science and Cybernetics field were analyzed using text mining methods and bibliometric tools. The analysis aims to evaluate the topics that have received significant attention in this field over the past ten years and to identify prominent subject headings for researchers, using advanced text mining techniques. In addition to traditional bibliometric analysis, a more in-depth thematic classification was performed using machine learning-based text mining techniques. The data were obtained from high-impact bibliometric sources such as Web of Science on 15/05/2025, in plain text and Excel formats. The top three countries in terms of publication volume in this field are China (80.90%), the USA (16.30%), and Australia (12.80%). The leading institutions include the Chinese Academy of Sciences (f=91, 9.10%), the Ministry of Education of China (f =73,7.30%), and Guangdong University of Technology (f=59, 5.90%). The two journals publishing the highest number of the top 1,000 cited works are IEEE Transactions on Cybernetics (53.00%) and IEEE Transactions on Systems, Man, and Cybernetics: Systems (29.70%).. Apart from Cybernetics, the most closely associated research areas were Automation and Control Systems (f=827,82.70%), Computer Science-Artificial Intelligence (f=599,59.90%), and Ergonomics (f=64,6.40%).
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.077 | 0.092 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".