The rise of online platforms has facilitated covert forms of explicit content, which pose significant challenges for detection and regulation. Often using coded language to bypass moderation, this content erodes user trust and may be associated with scam-related risks, posing direct financial and personal risks. In this study, we map the landscape of online explicit content posts, focusing on their categorization, linguistic strategies, and temporal and behavioral patterns as they appear within mainstream platform reddit. We investigated five distinct content categories including Virtual Services (VS), Physical Services (PS), Exhibitionism (Ex), Couples and Group Interactions (CGI), and Content Creation and Sales (CCS) and performedmed large-scale experimentation using state-of-the-art large language models (LLMs) such as GPT-4, LLaMA 3.3-70B-Instruct, Gemini 1.5 Flash, Mistral 8×7B, Qwen 2.5 Turbo, and Claude 3.5 Haiku. Our work demonstrates that a nuanced classification of these services requires moving beyond simple keywords, and we establish that expressive signals such as sentiment, emotion, and tone are critical features for accurate detection. Our analysis reveals the distinct behavioral and psychosocial expression patterns that characterize each service category, providing a robust framework for future moderation.

Answering natural language queries over relational data often requires retrieving and reasoning over multiple tables, yet most retrievers optimize only for query–table relevance and ignore table–table compatibility. We introduce REaR (Retrieve, Expand and Refine), a three-stage, LLM-free framework that separates semantic relevance from structural joinability for efficient, high-fidelity multi-table retrieval. REaR (i) retrieves query-aligned tables, (ii) expands these with structurally joinable tables via fast, precomputed column-embedding comparisons, and (iii) refines them by pruning noisy or weakly related candidates. Empirically, REaR is retriever-agnostic and consistently improves dense/ sparse retrievers on complex table QA datasets (BIRD, MMQA, and Spider) by improving both multi-table retrieval quality and downstream SQL execution. Despite being LLM-free, it delivers performance competitive with state-of-the-art LLM-augmented retrieval systems (e.g., ARM) while achieving much lower latency and cost. Ablations confirm complementary gains from expansion and refinement, underscoring REaR as a practical, scalable building block for table-based downstream tasks (e.g., Text-to-SQL).