Harmful Content Detection Papers

This page lists 1 peer-reviewed paper in the research area of Harmful Content Detection in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (1 of 1)

LLM-based Semantic Augmentation for Harmful Content Detection

Authors: Elyas Meguellati1, Assad Zeghina2, Shazia Sadiq1, Gianluca Demartini1

Year: 2025

Published in: ArXiv

Institution: University of Queensland, University of Strasbourg

Research Area: Natural Language Processing, Harmful Content Detection

Discipline: Natural Language Processing

The paper introduces an approach using LLM-based semantic augmentation for harmful content detection on social media, achieving performance comparable to human-annotated models but at reduced cost.

Methods: The researchers utilize LLMs to clean noisy text and generate explanations for context-rich preprocessing, then evaluate the augmented training sets on multiple high-context datasets such as SemEval 2024 Persuasive Meme, Google Jigsaw toxic comments, and Facebook hateful memes datasets.

Key Findings: The efficacy of LLM-based semantic augmentation in enhancing training sets for social media tasks such as propaganda detection, hateful meme classification, and toxicity identification.