A Scalable and Transparent Framework for Intelligent Social Media Moderation Using Blockchain and Federated Learning

Abstract

With social media platforms like Facebook booming in recent years, we’ve seen a big rise in issues like misinformation, hate speech, and other harmful content that impact millions of users around the world. Traditional ways of moderating content just can’t keep up with these challenges. They often fall short when it comes to being transparent, quick to respond, and scalable enough to handle everything. This paper introduces a fresh new approach that combines Blockchain, AI, and Machine Learning to tackle these problems head-on. The framework allows for real-time tracking and smart analysis, which helps to respond quickly to harmful activity on social media. By using blockchain to keep a permanent record of content, AI to understand the emotional tone and behavior behind posts, and machine learning to find patterns and spot unusual activities, this method presents a strong, decentralized solution to the moderation challenge. We back this up with a lot of data from Facebook, showing that this approach significantly improves our ability to spot and deal with threats on the spot while also making things clearer and boosting user trust.