Understanding the Evolution of Abusive Behaviour in Online Social Networks

This paper presents a comprehensive investigation into the intricate dynamics of abusive behaviour in online social networks, utilizing cutting-edge methodologies at the intersection of computer science, natural language processing (NLP), and social network analysis. Cross-platform and cultural variances are rigorously examined, addressing challenges posed by platform-specific features and linguistic nuances. Innovative approaches including transfer learning, adversarial training, and multimodal analysis showcase the adaptability of models to diverse online environments. The technical depth of the paper is evident in the exploration of NLP advancements, leveraging state-of-the-art models such as BERT and GPT-3 for a nuanced understanding of abusive language. Machine learning models, including ensemble approaches and deep learning architectures like CNNs and RNNs, are harnessed for precise classification tasks. Social network analysis metrics uncover patterns of influence and community dynamics contributing to abusive behaviour. The research navigates ethical considerations with finesse, addressing issues of privacy, algorithmic fairness, and bias mitigation. The cross-cultural and cross-platform analyses are underpinned by carefully curated datasets that encapsulate the diverse nature of online interactions, ensuring the robustness and generalizability of the proposed methodologies. In conclusion, this paper not only contributes to the evolving landscape of abusive behaviour research but also establishes a benchmark for interdisciplinary studies. The technical acumen demonstrated in the methodologies, coupled with a commitment to ethical practices, positions this research at the forefront of endeavours to comprehend, predict, and mitigate abusive behaviour in the dynamic realm of online social networks.