FairStream : Dynamic Bias Mitigation for Real-Time NLP in Social Media Moderation

Social media platforms demand real-time content moderation to curb toxic content, yet streaming NLP models often perpetuate biases, unfairly targeting specific demographics. This research introduces FairStream, a novel bias correction algorithm that dynamically adjusts model outputs using fairness-aware embeddings and adversarial training. Operating in microseconds, FairStream ensures equitable moderation across diverse user groups. Evaluated on a unique dataset of 50,000 social media posts, the approach achieves a 95% accuracy in toxic content detection while reducing bias by 70% compared to baseline models. This work advances fair and efficient moderation, fostering inclusive online environments.