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Kumar, Y., Huang, K., Perez, A., Yang, G., Li, J.J., Morreale, P., et al. (2024) Bias and Cyberbullying Detection and Data Generation Using Transformer Artificial Intelligence Models and Top Large Language Models. Electronics, 13, Article 3431.
https://doi.org/10.3390/electronics13173431

has been cited by the following article:

• TITLE: The Impact of AI on Social Media: Enhancing Content Moderation and Combating Misinformation

  AUTHORS: Andrew Mikhail Anthony, Hitendra Chaudhary, Olatunde Abiona, Clement Onime

  KEYWORDS: Artificial Intelligence, Social Media, Content Moderation, Misinformation, Human Oversight, Algorithmic Fairness

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.19 No.10, October 31, 2026

  ABSTRACT: Social media platforms face a persistent challenge: they must identify harmful or misleading material across immense volumes of text, images, and video while protecting legitimate expression. This paper reviews the potential and limitations of artificial intelligence (AI) in content moderation and proposes a framework that combines automated detection, human review, transparent procedures, and ongoing evaluation. Natural language processing and computer vision can help prioritize content for review, but errors, linguistic variation, bias, and rapidly changing misinformation tactics limit the reliability of automated decisions. The proposed framework routes ambiguous and consequential cases to trained moderators, provides users with explanations and appeals, and uses audits and feedback to improve performance. It is a conceptual proposal rather than an empirically validated system; future research should test its accuracy, fairness, and practical effects across languages and platforms.