Date of Award
2025
Document Type
Honors
Degree Name
General Beadle Honors Program
Department
Computer Science
Abstract
In the age of artificial intelligence, spam messages have become increasingly widespread and sophisticated. Their rapid evolution is driven by ongoing efforts to filter and block them, prompting spammers to constantly adapt their tactics. Since machine learning algorithms require time and data to retrain and adjust, it becomes essential for humans to step in and help classify messages manually when needed. This layered approach,referred to as defensive-indepth, adds multiple barriers through which spam and smishing messages must pass, reducing the likelihood of them reaching the end user. This case study explores the detection rates of various machine learning algorithms compared to different groups of human participants. Messages that are not correctly identified can result in missed information or, worse, users falling victim to hacking attempts. By examining human and machine learning performance in spam detection, this study underlines the importance of having a collaborative approach that leverages each group’s strengths.
Recommended Citation
Miller, Gabe, "Evaluating SMS Spam Classification: Human Judgement vs Machine Learning Models" (2025). Honors. 3.
https://scholar.dsu.edu/honors/3