Agent-Based User-Adaptive Filtering for Categorized Harassing Communication

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.

Figures (8)

• Figure 1: Individualized Agent between Social media platform and user helping to customize the filtering of communication to remove or flag online communication.
• Figure 2: Percentage of data selected by users to be filtered for the different harassment categories.
• Figure 3: Percentage of tweets selected to be filtered for each category and for perceived harassment intensity levels.
• Figure 4: Histogram of # users wanting to filter a certain number of tweets. X axis represents # of tweets (0-75), Y axis represents # of users wanting to filter that many tweets.
• Figure 5: (a) Frequency plot for filtering choices (X-axis: # users, out of 5, who chose to filter a tweet, Y-axis: # of tweets), (b) User Choice variability plot.