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@article{195331,
        author = {Talluri Ramesh Babu and Madineni Nandini and Talasila Greeshma and Vajrala Narendra Reddy},
        title = {Real Time Detection Of Evolving Spammer Groups In E-commerce},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {422-434},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195331},
        abstract = {E-commerce platforms are increasingly affected by spammer groups that manipulate product reviews to influence ratings and mislead customers. Detecting such coordinated activities is challenging, especially when spammers continuously change their behaviour. In this work, we propose a hybrid detection framework that combines graph-based modelling, behavioural analysis, and machine learning techniques to identify spammer groups. The system constructs a user product interaction graph to capture relationships among reviewers and applies TF-IDF with cosine similarity to analyse textual patterns. Community detection is used to identify coordinated reviewer groups, while a Random Forest model is used for classification. The results show that the proposed approach can effectively detect suspicious users and groups with improved accuracy and reduced false positives. This makes the system suitable for practical use in large-scale e-commerce platforms.},
        keywords = {Spammer Group Detection, E-commerce Reviews, Graph-based Modelling, Behavioural Feature Analysis, Machine Learning Classification, Real-time Spam Detection, Community Detection},
        month = {April},
        }