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.

本文通过分析计算机科学领域当前热门的研究领域，探讨了导致这些研究领域出现的因素。利用包括论文、引用和资助信息在内的综合数据集，我们采用了决策树和逻辑回归等先进的机器学习技术，预测研究领域的趋势。分析结果表明，研究论文中引用数量（引用计数）在决定研究领域趋势方面起着关键作用，成为推动计算机科学领域趋势的最相关因素。此外，国家科学基金和专利对热门话题的影响随时间增加。逻辑回归模型在预测趋势方面表现优于决策树模型，具有更高的准确率、精确率、召回率和 F1 分数。通过超过随机猜测的基线，我们的数据驱动方法显示出更高的准确性和效力，为研究人员和机构提供了基于数据的决策和未来研究方向的宝贵洞察。