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Leveraging LLMs for Context-Aware Implicit Textual and Multimodal Hate Speech Detection

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Updated 8 h ago · first seen 11 Sept 2026

paper_01M294G5HSW7AWXBHQ83K732DB

Published: 11 Sept 2026; T1 · 8 h ago
arXiv: 2510.15685; T1 · 8 h ago
Category: cs.CL; T1 · 8 h ago

Abstract

This paper investigates the use of an LLM to generate auxiliary background context for social media posts, and explores four methods to incorporate this context into the input of an SBERT-based Hate Speech Detection (HSD) classifier. These are: text concatenation, embedding concatenation, a hierarchical transformer-based fusion, and LLM-driven text enhancement. We evaluate the impact of our context generation and incorporation strategies in a textual setting on the Latent Hatred dataset of implicitly hateful tweets and a multimodal setting on the MAMI dataset of misogynous internet memes. Results are evaluated against a zero-context baseline, two previous approaches based on entity linking, and a zero-shot LLM classifier. Findings indicate that incorporating generated context improves HSD performance by up to 3 and 6 F1 points on textual and multimodal settings respectively, from a zero-context baseline to the highest-performing system, based on embedding concatenation.

Authors 2

Joshua Wolfe Brook, Ilia Markov

Specification

Official page: arxiv.org/abs/2510.15685
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Arxiv announce type: replace
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
arXiv id: 2510.15685
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Categories: cs.CL
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
PDF: arxiv.org/pdf/2510.15685
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Primary category: cs.CL
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 8 h agohigh

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Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
This paper investigates the use of an LLM to generate auxiliary background context for social media posts, and explores four methods to incorporate this context into the input of an SBERT-based Hate Speech Detection (HSD) classifier. These are: text concatenation, embedding concatenation, a hierarchical transformer-based fusion, and LLM-driven text enhancement. We evaluate the impact of our context generation and incorporation strategies in a textual setting on the Latent Hatred dataset of implicitly hateful tweets and a multimodal setting on the MAMI dataset of misogynous internet memes. Results are evaluated against a zero-context baseline, two previous approaches based on entity linking, and a zero-shot LLM classifier. Findings indicate that incorporating generated context improves HSD performance by up to 3 and 6 F1 points on textual and multimodal settings respectively, from a zero-context baseline to the highest-performing system, based on embedding concatenation.	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CL	feed	T1· Official	1 h ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.