REDDIX-NET: A Novel Dataset and Benchmark for Moderating Online Explicit Services

2025-05-29

AI Summary

Detecting covert illicit services—such as online prostitution—on digital platforms remains challenging due to their obfuscated linguistic and behavioral patterns. Method: We propose a fine-grained behavioral classification paradigm, introducing REDDIX-NET, the first benchmark dataset for sex-service intent detection, constructed from Reddit’s NSFW corpus and annotated with six nuanced user intent categories—extending beyond coarse-grained NSFW binary classification. Our approach integrates zero-shot and fine-tuned large language models (GPT-4, Gemini 1.5 Flash), PLM-driven sentiment and temporal comment sequence modeling, and structured metadata mining to uncover discriminative interaction patterns and a consistent diurnal activity peak (20:00–23:00). Contribution/Results: The framework achieves 92.3% accuracy on six-class intent classification, delivering an interpretable, operationally deployable solution for platform-level content governance and regulatory enforcement.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Mitigating online harms, including harassment, spam, and fake reviews
• Economics, Online Markets and Human Computation: The sharing economy

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. The dataset is derived from thousands of web-scraped NSFW posts on Reddit and categorizes users into six behavioral classes reflecting different service offerings and user intentions. We evaluate the classification performance of state-of-the-art large language models (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. These analyses reveal 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.

Problem

• Detecting and regulating covert online illicit activities like prostitution
• Evaluating LLM performance in classifying explicit service user behaviors
• Analyzing temporal and interaction patterns for AI-driven content moderation

Innovation

• Introduces REDDIX-NET dataset for online sexual services moderation
• Evaluates LLMs like GPT-4 for classification and sentiment analysis
• Uses metadata extraction to uncover behavioral and temporal patterns