Go to ACL ARR 2025 May homepageReddiX-NET: A Novel Dataset and Benchmark for Moderating Online Explicit Services
Abstract: The rise of online platforms has enabled covert illicit activities, including online prostitution, to pose challenges for detection and regulation. In this study, we introduce **ReddiX-NET**, a novel benchmark dataset specifically designed for moderating online sexual services and going beyond traditional NSFW filters, derived from thousands of web-scraped NSFW posts on Reddit, categorizing users into six behavioral classes reflecting different service offerings and user intentions. We evaluate the classification performance of state-of-the-art LLMs (GPT-4, LlaMA 3.3-70B-Instruct, Gemini 1.5 Flash, Mistral 8X7B, Qwen 2.5 Turbo, Claude 3.5 Haiku) using advanced quantitative metrics, finding promising results with models like GPT-4 and Gemini 1.5 Flash. Beyond classification, we conduct sentiment and comment analysis, leveraging LLM and PLM-based approaches and metadata extraction to uncover behavioral and temporal patterns, revealing peak engagement times and distinct user interaction styles across categories. Our findings provide critical insights into AI-driven moderation and enforcement, offering a scalable framework for platforms to combat online prostitution and associated harms.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: AI for social good, online sexual services, social media
Contribution Types: Data analysis
Languages Studied: English
Submission Number: 6891
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