Academic

Agent-Based User-Adaptive Filtering for Categorized Harassing Communication

arXiv:2603.13288v1 Announce Type: new Abstract: 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.

Z
Zenefa Rahaman, Sandip Sen · · 1 min read · 6 views
#cs.AI

arXiv:2603.13288v1 Announce Type: new Abstract: 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.

Executive Summary

The article introduces an innovative agent-based framework designed to improve personalized filtering of harassing communication online. Rather than applying uniform moderation rules, the system employs adaptive filtering agents that evolve in response to user feedback, adjusting thresholds across harassment categories such as offensive, abusive, and hateful content. The authors validate their approach through supervised classification and simulated user data, demonstrating enhanced filtering precision and user satisfaction relative to static models. This work advances the field by proposing a scalable, user-centric solution that balances content moderation with user autonomy.

Key Points

  • ▸ Agent-based adaptive filtering agents replace global moderation rules
  • ▸ Agents learn from user feedback and dynamically adjust filtering thresholds across categories
  • ▸ Experimental validation shows improved precision and user satisfaction

Merits

Strength in Personalization

The adaptive agent model introduces a significant leap in user-centric content moderation by aligning filtering with individual preferences and feedback.

Demerits

Scalability Concern

While effective in simulated environments, the framework’s real-world scalability and computational overhead in handling millions of user interactions remain unaddressed.

Expert Commentary

This article represents a meaningful advancement in the intersection of AI and content governance. The shift from static moderation to adaptive, agent-driven filtering marks a paradigm shift—one that aligns more closely with the dynamic, personalized nature of online discourse. The authors rightly emphasize the importance of user feedback as a signal for continuous adaptation, which is critical in an environment where harassment evolves in semantics and context. However, the absence of empirical data on real user behavior limits the generalizability of results. Additionally, potential for algorithmic bias in adaptive thresholds—particularly if feedback loops reinforce subjective perceptions of harassment—requires careful mitigation. If properly calibrated with diverse, representative input data, this framework could become a foundational tool in ethical, scalable content moderation.

Recommendations

  • ✓ 1. Conduct longitudinal studies with real user data to validate scalability and bias mitigation
  • ✓ 2. Integrate explainability interfaces to allow users to understand and adjust filtering thresholds transparently

Sources

Related Articles