TikTok AgeGuard

Inspiration

People often lie about their age when signing up for applications, which can expose them to inappropriate content that is not suitable for their age. This issue needs to be addressed. We plan to do exactly that with this project.

What it does

TikTok AgeGuard is an innovative solution designed to predict a user's age based on their phone activity and subsequently moderate the content displayed on TikTok to ensure age-appropriate experiences. This project combines machine learning, data analysis, and content filtering to create a safer and more suitable environment for users of different age groups.

Predict User's Age: Develop a model that accurately predicts the user's age based on their phone activity data. Content Censorship: Implement an intelligent system that filters TikTok content to match the predicted age group, ensuring age-appropriate viewing. Enhance User Safety: Provide a safer and more appropriate digital environment for young users.

Steps to Implement

Data Collection:

Develop a mobile app to collect phone activity data with user consent. Ensure data privacy and security compliance (e.g., GDPR, CCPA). Model Development:

Preprocess the collected data to create features relevant to age prediction. Train and validate different machine learning models to identify the most accurate one. Content Analysis:

Use NLP and Computer Vision to analyze and categorize TikTok videos based on age-appropriateness. Create a database of categorized content. Integration:

Integrate the age prediction model and content filtering algorithm with the TikTok API. Develop real-time content moderation logic to adjust the content feed dynamically. Testing and Validation:

Conduct thorough testing to ensure the accuracy and reliability of the age prediction model. Validate the effectiveness of the content filtering algorithm in displaying appropriate content. Deployment:

Deploy the solution on cloud platforms like AWS/GCP/Azure for scalability and reliability. Release the mobile app for public use with clear instructions on data privacy and usage.

Potential Challenges

Data Privacy and Security: Ensuring user data is collected, stored, and processed securely and in compliance with regulations. Model Accuracy: Achieving high accuracy in age prediction from diverse phone activity patterns. Content Classification: Effectively classifying TikTok content into appropriate categories using NLP and Computer Vision.

Expected Outcomes

A functional prototype of an app that predicts user age from phone activity. An integrated system that censors TikTok content based on the predicted age. Improved safety and age-appropriate content consumption for TikTok users.

Key Features

Phone Activity Data Analysis: Collect and analyze phone activity data such as app usage patterns, screen time, browsing history, and interaction behavior. Machine Learning Model: Train a machine learning model to predict the user's age from the collected data. Content Filtering Algorithm: Create a content filtering algorithm that classifies TikTok videos into different age-appropriate categories. Real-Time Moderation: Implement real-time content moderation to dynamically adjust the content feed based on the predicted age.

Conclusion

TikTok AgeGuard aims to leverage the power of machine learning and content moderation to create a safer and more suitable TikTok experience for users of all ages. By accurately predicting user age and censoring content accordingly, this project addresses a significant concern in the realm of social media consumption.

Built With

• mongodb
• node.js
• python
• react-native
• tensorflow