Content Filtering for Parental Control

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Project Overview:

Objective

The “Content Filtering for Parental Control” project aims to create a dataset for training machine learning models to accurately classify and filter digital content to ensure age-appropriate and safe online experiences for children. This dataset will empower parental control systems to safeguard children from harmful or inappropriate content.

Scope

This project involves collecting digital content data from various sources, including websites, online platforms, and social media, and annotating them with appropriate content labels and age restrictions.

Sources

Websites and Online Platforms: Gather data from websites, online games, streaming platforms, and social media networks that feature a wide range of content.
User Contributions: Encourage users and parents to submit content for classification and filtering.
Publicly Available Databases: Utilize publicly available datasets containing digital content, if applicable.

Data Collection Metrics

Total Digital Content Items: 50,000 items
Websites and Online Platforms: 30,000
User Contributions: 15,000
Public Databases: 5,000 (if available)

Annotation Process

Stages

Content Labeling: Annotate each digital content item with labels indicating the appropriateness for different age groups (e.g., child, teen, adult) and specific content categories (e.g., violence, nudity, drug use).
Metadata Logging: Log metadata, including the source of the content, upload date, and user ratings (if available).

Annotation Metrics

Digital Content Items with Labels: 50,000
Metadata Logging: 50,000

Quality Assurance

Stages

Annotation Verification: Implement a validation process involving content moderation experts to review and verify the accuracy of content labels.
Data Quality Control: Ensure the removal of irrelevant or duplicate content items from the dataset.
Data Security: Protect sensitive information and adhere to privacy regulations, especially when dealing with user-contributed content.

QA Metrics

Annotation Validation Cases: 5,000 (10% of total)
Data Cleansing: Remove irrelevant or duplicate content items

Conclusion

The “Content Filtering for Parental Control” dataset is a vital resource for enhancing online safety for children. With accurately labeled digital content and comprehensive metadata, this dataset empowers parental control systems to effectively filter out harmful or inappropriate content, ensuring age-appropriate online experiences. It provides a foundation for the development of advanced content classification and filtering algorithms and tools that can support parents in safeguarding their children’s digital activities and promoting a safer online environment for young users.

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