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Agent-Based User-Adaptive Filtering for Categorized Harassing Communication

arXiv AI Archived Mar 17, 2026 ✓ Full text saved

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, a

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Computer Science > Artificial Intelligence [Submitted on 28 Feb 2026] Agent-Based User-Adaptive Filtering for Categorized Harassing Communication Zenefa Rahaman, Sandip Sen 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. Comments: 10 pages, 59 figures. Revised and archived version of ALA 2019 workshop paper (co-located with AAMAS 2019) Subjects: Artificial Intelligence (cs.AI) ACM classes: I.2.11; H.3.3; K.4.1 Cite as: arXiv:2603.13288 [cs.AI] (or arXiv:2603.13288v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.13288 Focus to learn more Submission history From: Zenefa Rahaman Dr. [view email] [v1] Sat, 28 Feb 2026 01:52:48 UTC (2,329 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev | next > new | recent | 2026-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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