Research Paper Quality Recognition

Research Paper Quality Recognition

Presenter(s)

Sadwik Gummadavelli

Comments

11:40-12:00, LTC Studio

Description

Knowledge and innovations are shaped by using the quality and credibility of the scientific research. There always remains a challenge how to distinguish between impactful high-quality research and flawed. This project proposes a very systematic approach to classifying the research papers into good and bad categories where bad papers are those retracted from journals or conference proceedings and good papers are characterized by high citation counts. We explore the underlying factors that contribute to a papers scholarly influence or its eventual rejection by analyzing by citation patterns publication meta data and retraction records. We used machine learning models and feature extraction techniques to identify anomalies, trends and potential predictors of research quality. The findings of our study insights into highlighting the importance of citation behavior, the dynamics of academic publishing and scientific accountability. This study adds to the larger conversation about academic impact evaluation and lays the groundwork for automated tools that can help assess the reliability of research papers.

Publication Date

4-23-2025

Project Designation

Course Project - CPS 596 P3

Primary Advisor

Tam Nguyen

Primary Advisor's Department

Computer Science

Keywords

Stander Symposium, College of Arts and Sciences

Institutional Learning Goals

Scholarship; Community; Vocation

Research Paper Quality Recognition

Share

COinS