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As online social platforms continue to proliferate, the menace of social spam has become a pressing concern, necessitating effective detection mechanisms. This chapter provides a comprehensive review of the current landscape of Social Spam Detection (SSD), focusing on the pivotal role played by machine learning approaches. We delve into the advancements and challenges associated with leveraging machine learning algorithms for identifying and mitigating social spam. The review encompasses a thorough examination of various techniques, methodologies, and datasets employed in recent studies. Additionally, we highlight emerging trends, gaps in existing research, and potential avenues for future exploration in the dynamic realm of SSD. This review serves as a valuable resource for researchers, practitioners, and industry stakeholders seeking insights into the evolving field of Machine (M) Learning-driven SSD.

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A Survey on Exploring Advances and Challenges in Social Spam Detection Through Machine Learning Approaches

  • 会议论文

  • 发表日期：

    2025年

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  • M. Sivapratap Reddy,
  • P. Phanindra Kumar Reddy

摘要

As online social platforms continue to proliferate, the menace of social spam has become a pressing concern, necessitating effective detection mechanisms. This chapter provides a comprehensive review of the current landscape of Social Spam Detection (SSD), focusing on the pivotal role played by machine learning approaches. We delve into the advancements and challenges associated with leveraging machine learning algorithms for identifying and mitigating social spam. The review encompasses a thorough examination of various techniques, methodologies, and datasets employed in recent studies. Additionally, we highlight emerging trends, gaps in existing research, and potential avenues for future exploration in the dynamic realm of SSD. This review serves as a valuable resource for researchers, practitioners, and industry stakeholders seeking insights into the evolving field of Machine (M) Learning-driven SSD.