Leveraging LLMs for Context-Aware Implicit Textual and Multimodal Hate Speech Detection
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
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
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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8 h ago
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- Authors
- Joshua Wolfe Brook, Ilia Markov
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| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| https://arxiv.org/pdf/2510.15685 | → 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 →
- New paperPaperLeveraging LLMs for Context-Aware Implicit Textual and Multimodal Hate Speech Detection
New paper: Leveraging LLMs for Context-Aware Implicit Textual and Multimodal Hate Speech Detection
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CL | feed | T1· Official | 6 h ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.