As social media platforms continue to grow in scale and influence, they are increasingly used to spread not only positive content but also harmful and inappropriate material. Traditional content moderation methods, which rely heavily on manual review, are often expensive, time-consuming, and lack the scalability required to keep up with the volume of user-generated content. This has prompted a shift toward automated, AI-driven moderation systems. In this work, presented is a technical overview of an AI-powered framework designed to moderate user content on social platforms efficiently. The process begins with collecting large volumes of data from various social media sources, which is then stored in a centralized database for further processing and analysis. The next stage involves preprocessing this raw data to eliminate irrelevant or noisy content, such as advertisements, bot-generated text, and unrelated user comments. This cleaning step ensures that only high-quality, relevant data is used to train the machine learning models. Once prepared, the dataset is used to train deep learning models capable of identifying patterns and features associated with harmful or policy-violating content. These models are trained to recognize multiple categories of toxic content, including but not limited to hate speech, spam, and explicit imagery. Importantly, the system incorporates contextual and cultural sensitivity to reduce false positives and improve classification accuracy across diverse user bases. Following training, the models are integrated into a post-level classification pipeline. When a new post is submitted, it is evaluated by the system and assigned likelihood scores across different content categories. If the score for any harmful category surpasses a predefined threshold, the content is flagged for further action, either for automated removal or human review, depending on severity and confidence levels. This framework not only enhances moderation efficiency but also supports real-time response to violations, helping platforms maintain safer and more respectful online environments at scale.

Machine Learning Classification Advertisements Traditional Data Collection