Spam Message Detection Over Social Media: a Supervised Sampling Approach for the Social Web of Things
Year: 2025
Type: article
Abstract: The increasing use of social media has led to a surge in spam messages, including fake advertisements, phishing links, and misinformation. Traditional spam detection methods struggle with evolving spam patterns and imbalanced datasets, where spam messages constitute only a small fraction of total messages. This paper proposes a supervised sampling approach for spam detection in the Social Web of Things (SWoT), leveraging machine learning and natural language processing (NLP) techniques. The system uses Synthetic Minority Oversampling Technique (SMOTE) and cost-sensitive learning to handle class imbalance, improving the classification accuracy of spam detection models. Experimental results on real-world social media datasets demonstrate that the proposed approach enhances spam detection performance, reducing false positives and improving precision.
Source: Journal of engineering sciences.
Authors: PURETI. MANGATAYARU, NARESH
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409206771