Agent-Based User-Adaptive Filtering for Categorized Harassing Communication
This work addresses content moderation for users in online social networks, but it is incremental as it builds on existing supervised classification techniques with simulated data.
The paper tackles the problem of filtering harassing communication in online social networks by proposing an agent-based framework that models user-specific tolerance levels and preferences, resulting in improved filtering precision and user satisfaction compared to static models.
We propose an agent-based framework for personalized filtering of categorized harassing communication in online social networks. Unlike global moderation systems that apply uniform filtering rules, our approach models user-specific tolerance levels and preferences through adaptive filtering agents. These agents learn from user feedback and dynamically adjust filtering thresholds across multiple harassment categories, including offensive, abusive, and hateful content. We implement and evaluate the framework using supervised classification techniques and simulated user interaction data. Experimental results demonstrate that adaptive agents improve filtering precision and user satisfaction compared to static models. The proposed system illustrates how agent-based personalization can enhance content moderation while preserving user autonomy in digital social environments.