发表机构
Pak-Austria Fachhochschule, Institute of Applied Sciences and Technology（巴基斯坦-奥地利应用科学与技术应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对仇恨言论检测中可解释性不足和多数据集泛化问题，提出融合DistilBERT、BiLSTM和注意力机制的可解释框架，利用LIME解释预测，在二元和多类任务上均优于基线。
AI 中文摘要
社交媒体上的仇恨言论对社会和谐、心理健康和公共安全构成严重风险，因此及时准确地检测仇恨言论对于内容审核系统至关重要。大多数现有研究集中于二元分类，在单一数据集上评估其框架，并且对决策过程的洞察有限，这限制了其实际应用性。此外，关于预测推理的可解释性研究也很有限。为解决这些挑战，本研究提出了一个多层次且可解释的仇恨言论检测框架。所提出的模型将DistilBERT（蒸馏双向编码器表示来自变换器）嵌入与Bi-LSTM（双向长短期记忆）模型以及注意力机制相结合，以捕捉文本中的上下文含义和序列依赖性。为增强信任和透明度，采用LIME（局部可解释模型无关解释）通过突出有影响力的文本特征来解释模型预测。该框架在两个基准数据集上使用二元和多类分类进行评估，以检验其鲁棒性和泛化能力。此外，还进行了消融研究以突出所提出框架各组件的重要性。对于二元分类，所提出的模型在Davidson数据集上达到96.78%的F1分数，在SMHS数据集上达到99.53%的F1分数。在多类设置中，它在Davidson和SMHS数据集上分别达到97.00%和94.99%的F1分数，优于现有基线方法。结果表明，多层次评估提高了可靠性，所提出的框架有效平衡了性能与效率。这使得该框架适用于需要准确、可泛化和可解释决策的实际仇恨言论审核系统。
英文摘要
Hate speech on social media poses serious risks to social harmony, mental well-being, and public safety, making its timely and accurate detection essential for content moderation systems. Most existing studies focus on binary classification, evaluated their frameworks on a single dataset, and provide limited insight into how decisions are made, which limits their real-world applicability. In addition, limited work is done on the explainability of their predictive inference. To address these challenges, this study proposes a multilevel and explainable hate speech detection framework. The proposed model integrates DistilBERT (Distilled Bidirectional Encoder Representations from Transformers) embeddings with a Bi-LSTM (Bidirectional Long Short-Term Memory) model, and an attention mechanism to capture both contextual meaning and sequential dependencies in text. To enhance trust and transparency, LIME (Local Interpretable Model-agnostic Explanations) is employed to explain model predictions by highlighting influential textual features. The framework is evaluated on two benchmark datasets using both binary and multi-class classification to examine robustness and generalization. In addition, an ablation study is presented to highlight the significance of various components of proposed framework. For binary classification, the proposed model achieves F1-scores of 96.78% on the Davidson dataset and 99.53% on the SMHS dataset. In the multi-class setting, it attains F1-scores of 97.00% and 94.99% on the Davidson and SMHS datasets, respectively, outperforming existing baseline approaches. The results demonstrate that multilevel evaluation improves the reliability that the proposed framework effectively balances performance and efficiency. This makes the framework suitable for practical hate speech moderation systems that require accurate, generalizable, and explainable decisions.
Comments20 pages, 12 figures, 6 tables
