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Social Media Tech•January 23, 2026

AI Cyberbullying Detection App

ai basec cyberbullyind detection application tha detect the cyberbullyinng in viddeos comment live streaming video and more parent mode child mode school organization mode and etc

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Evaluation Scores

7.1/10
Good

Overall Score

7
Solution
8
Problem
8
Features
8
Market
7
Revenue
6
Competition
6
Risk

Market Analysis

Solution Overview

This AI-based cyberbullying detection application identifies and flags harmful content in videos, live streaming, and comments, providing a safe environment for users. It offers multiple modes, including parent, child, and school organization modes. The application utilizes machine learning algorithms to detect and prevent cyberbullying.

Problem Statement

Cyberbullying is a growing concern, affecting millions of people worldwide, particularly children and teenagers. It can lead to serious mental health issues, social isolation, and even suicide. Existing solutions often rely on manual reporting, which can be ineffective and slow.

Key Features

  • AI-powered detection
  • Multi-mode support
  • Real-time monitoring
  • Customizable alerts
  • User-friendly interface
  • Data analytics

Market Snapshot

  • Market Size: $10 billion (growing 20% annually)
  • Target Users: Parents, schools, and social media platforms
  • Growth Rate: 20% annually

Monetization Ideas

  • Subscription model: Offer monthly or yearly subscriptions to individuals and organizations
  • Advertising: Display relevant, non-intrusive ads within the application
  • Data analytics: Sell anonymized data and insights to researchers and organizations
  • Partnerships: Collaborate with social media platforms and schools to offer integrated solutions

Competitive Edge

The application's AI-powered detection and multi-mode support set it apart from existing solutions. Its customizable alerts and user-friendly interface also provide a unique selling point. However, the market is highly competitive, and the application must continually improve to stay ahead.

Risk Factors

  • Regulatory challenges
  • Competition from established players
  • Technical difficulties in AI model development

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