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

TL;DR

This paper introduces REDDIX-NET, a new dataset and benchmark for moderating online sexual services, utilizing large language models to classify user behavior and analyze engagement patterns for better AI-driven moderation.

Contribution

The paper presents REDDIX-NET, a novel dataset and benchmark specifically designed for detecting online sexual services, and evaluates multiple large language models for classification and behavioral analysis.

Findings

GPT-4 and Gemini 1.5 Flash achieved high classification accuracy.

Distinct user interaction patterns were identified across categories.

Peak engagement times vary by service category.

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.