Towards a Multimodal, Real-Time, Individualized Moderation System for AI-Enhanced User Security and Privacy on Social Media
Cici Ling
Faculty Mentor
Cici Ling (Luddy School of Informatics, Computing and Engineering)
Project Description
This project proposes the development of a multimodal, real-time, individualized moderation system to enhance user security and privacy on social media. By leveraging AI techniques, the system process and analyze data in different modalities, including text, image, and video to detect and mitigate human-factor security threats such as weaponized memes, hate speech, and coordinated manipulation in real-time. The individualized framework ensures that moderation adapts to user-specific communication styles and moderation preferences, promoting safety and equity of online interactions.
Technology or Computational Component
This project is building a smart moderation system that helps platforms like TikTok, Instagram, and group chats automatically detect and respond to harmful or unsafe content (like hate speech or violent memes) in real-time. What makes it different and advanced are three things: 1. Multimodal AI: The system understands not just text, but also images and videos—just like how people communicate online using memes, voice messages, or reaction videos. 2. Real-Time Response: Instead of waiting for human moderators, the system can act instantly, using AI models (like transformers and large language models) to detect threats while conversations happen. 3. Individualized Moderation: The system learns to adjust its behavior based on each user's personal communication style and preferences, so it’s not a one-size-fits-all solution. The system will be built using cloud computing (Amazon AWS), machine learning models trained on real-world data, and a user-friendly interface that shows how the moderation works.