使用基于翻译的方法与大语言模型进行社交媒体多语言仇恨言论检测
Multilingual Hate Speech Detection in Social Media Using Translation-Based Approaches with Large Language Models
AI总结：
本文构建英、乌、西三语仇恨言论推文数据集，并结合注意力层、传统模型、transformer与GPT/Qwen等LLM进行基准评估，显著提升多语言检测性能。
摘要：
社交媒体平台是公共话语的关键空间，塑造着观点与社区动态，但其广泛使用也放大了有害内容，尤其是仇恨言论，威胁在线安全与包容性。尽管仇恨言论检测在英语和西班牙语等语言中已得到广泛研究，乌尔都语仍未被充分探索，特别是在使用基于翻译的方法方面。为填补这一空白，我们引入了一个三语数据集，包含通过关键词过滤收集的10,193条推文，其中英语3,833条、乌尔都语3,197条、西班牙语3,162条，并在4,849个Hateful标签与5,344个Not-Hateful标签之间保持均衡分布。我们的方法将注意力层作为基于transformer的模型和大语言模型（LLM）的前置组件，以增强多语言仇恨言论检测的特征提取。对于非transformer模型，我们使用TF-IDF进行特征提取。研究使用最先进模型对该数据集进行基准测试，包括GPT-3.5 Turbo和Qwen 2.5 72B，以及SVM等传统机器学习模型和BERT、RoBERTa等其他transformer。三名标注员遵循严格指南，确保了较高数据集质量，Fleiss' Kappa达到0.821。我们将注意力层与GPT-3.5 Turbo和Qwen 2.5 72B相结合的方法取得了强劲性能：英语macro F1为0.87（GPT-3.5 Turbo），西班牙语为0.85（GPT-3.5 Turbo），乌尔都语为0.81（Qwen 2.5 72B），联合多语言模型为0.88（Qwen 2.5 72B）。这些结果表明，英语相对SVM基线0.80提升8.75%，西班牙语相对SVM基线0.78提升8.97%，乌尔都语相对SVM基线0.77提升5.19%，联合多语言模型相对SVM基线0.82提升7.32%。我们的框架为多语言仇恨言论检测提供了稳健解决方案，有助于在全球范围内促进更安全的数字社区。
英文摘要：
Social media platforms are critical spaces for public discourse, shaping opinions and community dynamics, yet their widespread use has amplified harmful content, particularly hate speech, threatening online safety and inclusivity. While hate speech detection has been extensively studied in languages like English and Spanish, Urdu remains underexplored, especially using translation-based approaches. To address this gap, we introduce a trilingual dataset of 10,193 tweets in English (3,834 samples), Urdu (3,197 samples), and Spanish (3,162 samples), collected via keyword filtering, with a balanced distribution of 4,849 Hateful and 5,344 Not-Hateful labels. Our methodology leverages attention layers as a precursor to transformer-based models and large language models (LLMs), enhancing feature extraction for multilingual hate speech detection. For non-transformer models, we use TF-IDF for feature extraction. The dataset is benchmarked using state-of-the-art models, including GPT-3.5 Turbo and Qwen 2.5 72B, alongside traditional machine learning models like SVM and other transformers (e.g., BERT, RoBERTa). Three annotators, following rigorous guidelines, ensured high dataset quality, achieving a Fleiss' Kappa of 0.821. Our approach, integrating attention layers with GPT-3.5 Turbo and Qwen 2.5 72B, achieves strong performance, with macro F1 scores of 0.87 for English (GPT-3.5 Turbo), 0.85 for Spanish (GPT-3.5 Turbo), 0.81 for Urdu (Qwen 2.5 72B), and 0.88 for the joint multilingual model (Qwen 2.5 72B). These results reflect improvements of 8.75% in English (over SVM baseline 0.80), 8.97% in Spanish (over SVM baseline 0.78), 5.19% in Urdu (over SVM baseline 0.77), and 7.32% in the joint multilingual model (over SVM baseline 0.82). Our framework offers a robust solution for multilingual hate speech detection, fostering safer digital communities worldwide.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
