Published Paper Details:

In the digital age, online social media platforms have become prime targets for malicious actors to disseminate spam through deceptive reviews, undermining the trustworthiness of content and user engagement. This paper presents Network-Centric Spam Detection: A Framework for Identifying Malicious Review Patterns in Online Social Media, a comprehensive approach that leverages network analysis to uncover suspicious behaviors and relationships among users and reviews. Unlike traditional content-based or behavior-based detection methods, the proposed framework models review interactions as a graph structure, enabling the identification of hidden spam campaigns through community detection, centrality metrics, and link prediction techniques. Experimental results on real-world datasets demonstrate the effectiveness of the framework in isolating coordinated spam networks with high precision and recall, showcasing its potential to enhance spam mitigation strategies across diverse platforms. This work contributes to the evolving field of trustworthy social computing by emphasizing the importance of relational patterns in spam detection.