  This paper explores the current trending research areas in the field of
Computer Science (CS) and investigates the factors contributing to their
emergence. Leveraging a comprehensive dataset comprising papers, citations, and
funding information, we employ advanced machine learning techniques, including
Decision Tree and Logistic Regression models, to predict trending research
areas. Our analysis reveals that the number of references cited in research
papers (Reference Count) plays a pivotal role in determining trending research
areas making reference counts the most relevant factor that drives trend in the
CS field. Additionally, the influence of NSF grants and patents on trending
topics has increased over time. The Logistic Regression model outperforms the
Decision Tree model in predicting trends, exhibiting higher accuracy,
precision, recall, and F1 score. By surpassing a random guess baseline, our
data-driven approach demonstrates higher accuracy and efficacy in identifying
trending research areas. The results offer valuable insights into the trending
research areas, providing researchers and institutions with a data-driven
foundation for decision-making and future research direction.
