Mapping the Landscape of Abusive Content Detection in Social Networks: A Comprehensive and Scientometric Analysis

ABSTRACT Rise of online social networks has transformed how people interact, exchange information and connect with one another. But this digital evolution has also brought forth a significant challenge: the proliferation of abusive content. Detecting as well as mitigating abusive content is important for fostering a safe and inclusive online environment. This survey provides a comprehensive overview of the state‐of‐the‐art methods for abusive content detection in online social networks. The paper begins by defining abusive content and its various manifestations in the digital realm and then delves into the evolving landscape of online social networks, highlighting the unique challenges posed by their dynamic and user‐generated nature. Firstly, a scientometric analysis of the literature pertaining to the last 30 years (1993–2023) has been performed through which a deep analysis of prominent keywords, documents, institutions, and countries have been conducted. Further, this survey explores the key approaches to abusive content detection, including machine learning methods, natural language processing techniques, and deep learning models. The importance of dataset curation and annotation, which play a pivotal role in training robust and effective models, has been discussed. The survey also highlights various challenges, ethical implications, and future research directions that can guide the development of more effective and responsible abusive content detection systems in social networks.