Mapping Computer Science Research: Trends, Influences, and Predictions

TL;DR

This study uses machine learning on a comprehensive dataset to identify key factors influencing trending research areas in computer science, highlighting the importance of reference counts and funding influences.

Contribution

It introduces a data-driven approach employing Decision Tree and Logistic Regression models to predict trending CS research areas based on citation and funding data.

Findings

Reference count is the most influential factor in trend prediction.

Logistic Regression outperforms Decision Tree in accuracy.

Funding sources like NSF grants and patents increasingly impact trends.

Abstract

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.