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AI-Driven Topic Modeling of Research Trends in Computer Science (2000–2025): A Longitudinal Analysis of arXiv Data | ||
| Al-Noor Journal for Information Technology and Cybersecurity | ||
| Article 3, Volume 2, Issue 2, December 2025, Pages 15-23 PDF (557.68 K) | ||
| Document Type: Research paper | ||
| DOI: 10.69513/jncs.v2.i2.a3 | ||
| Author | ||
| Marwan Tareq Al Jumaili* | ||
| Ministry of Justice | ||
| Abstract | ||
| The evolution of computer science over the last quarter of a century calls for detailed scrutiny if we are to successfully identify the shifts in focus and emerging areas of research that the analysis aims to capture. Taking advantage of Artificial Intelligence, and in particular topic modeling, we analyze the evolution of computer science research between 2000 and 2025, examining the arXiv database, which contains roughly 2.5 million preprints, about 40% of which belong to computer science (cs.* categories). Contextualized Topic Modeling (CTM) is correlation-based topic modeling. Using a more advanced correlation-based technique called Correlation Explanation (CorEx), we differentiated key topics, evaluated the shifts over set periods, and observed the rise and fall of topics such as deep learning, NLP, and quantum computing [5]. Thus, looking at our results and the six tables that overview the topics and the models, trends in subfields and performance indicators, and the eleven graphs that detailed distributions of topic trends along those lines, subfield trends to coherence, and interdisciplinary honing, which complement this analysis, it becomes completely evident from our results: there is greater, situational dependence of AI subfields; there is an overall decline in traditional methods; and there is a burgeoning up trend in new fields. That is important for researchers, policy makers, and industry to recognize and understand the drivers of the future of computer science. | ||
| Keywords | ||
| Artificial Intelligence; Topic Modeling; CorEx; CTM; Research Trends | ||
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