AI-ENHANCED CONTENT MODERATION SYSTEM FOR SOCIAL NETWORKS

Abstract

The present invention discloses an AI-enhanced content moderation system for social networks, utilizing a multi-modal approach that integrates natural language processing (NLP), computer vision, and audio analysis to detect and manage harmful user-generated content in real-time. The system processes diverse content types, including text, images, videos, and audio, using advanced machine learning models to identify inappropriate language, explicit visuals, and abusive speech. A decision engine aggregates the analysis results to determine moderation actions like flagging, blocking, or review. With a feedback loop for continuous learning and federated learning for privacy preservation, the system offers a scalable, accurate, and adaptive solution for maintaining safe and respectful online environments.

Specification

Description:The embodiments of the present invention generally relates to an advanced content moderation system for social networks utilizing artificial intelligence (AI). Specifically, it combines natural language processing (NLP), computer vision, and machine learning algorithms to detect, filter, and manage harmful, inappropriate, or prohibited user-generated content across various formats such as text, images, audio, and video. This system aims to enhance the accuracy, scalability, and efficiency of content moderation, addressing challenges posed by the vast volume and diversity of content on social platforms.
BACKGROUND OF THE INVENTION
The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present d , Claims:1. A method for AI-enhanced content moderation, comprising:
capturing user-generated content from a social network;
pre-processing the content for analysis;
analyzing the content using an AI analysis module, including NLP for text, computer vision for images/videos, and audio analysis for audio streams;
aggregating analysis results in a decision engine to determine appropriate moderation actions;
executing moderation actions and updating AI models through a feedback loop.

2. The method of claim 1, wherein the preprocessing step for text content includes natural language processing tasks such as tokenization, stemming, lemmatization, and removal of stop words to enhance the accuracy of text analysis.

3. The method of claim 1, wherein the step of analyzing image and video content using the computer vision unit involves applying convolutional neural networks (CNNs) and object detection algorithms, including YOLO (You Only Look Once) or SSD (Single Shot Detector), to identify explicit imagery, violence, and oth

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