This project analyzes community moderation and reporting data to identify recurring violations, content trends, and moderation outcomes. The goal is to transform raw moderation logs into structured insights that support operational, HR, and trust & safety decision-making.
- Anonymized moderation data
- Fields: report_id, content_type, violation_category, action_taken, report_date
- Total records: 300+
- Data loading and inspection using Pandas
- Data cleaning and categorization
- Frequency analysis of violation categories and moderation actions
- Summary reporting for operational insights
- Identified the most frequent violation categories in the community
- Observed moderation action patterns (removal vs warning)
- Findings can support policy improvement and workflow optimization
- Python
- Pandas
- Jupyter Notebook
- Google Colab
This project uses Anonymous data from certain community for demonstration purposes and does not contain any sensitive or private information.