Media bias detection is a critical task in ensuring fair and balanced information dissemination, yet it remains challenging due to the subjectivity of bias and the scarcity of high-quality annotated data. In this work, we perform sentence-level bias classification by fine-tuning a RoBERTa-based model on the expert-annotated BABE dataset. Using McNemar's test and the 5x2 cross-validation paired t-test, we show statistically significant improvements in performance when comparing our model to a domain-adaptively pre-trained DA-RoBERTa baseline. Furthermore, attention-based analysis shows that our model avoids common pitfalls like oversensitivity to politically charged terms and instead attends more meaningfully to contextually relevant tokens. For a comprehensive examination of media bias, we present a pipeline that combines our model with an already-existing bias-type classifier. Our method exhibits good generalization and interpretability, despite being constrained by sentence-level analysis and dataset size because of a lack of larger and more advanced bias corpora. We talk about context-aware modeling, bias neutralization, and advanced bias type classification as potential future directions. Our findings contribute to building more robust, explainable, and socially responsible NLP systems for media bias detection.

翻译：媒体偏见检测是确保信息公平公正传播的关键任务，但由于偏见的主观性及高质量标注数据的稀缺性，该任务仍面临挑战。本研究通过在专家标注的BABE数据集上微调基于RoBERTa的模型，实现了句子级别的偏见分类。通过McNemar检验和5x2交叉验证配对t检验，我们证明相较于领域自适应预训练的DA-RoBERTa基线模型，本模型在性能上取得了统计学意义上的显著提升。此外，基于注意力的分析表明，我们的模型避免了常见缺陷（如对政治敏感术语的过度反应），而是更有意义地关注上下文相关的标记。为全面审视媒体偏见，我们提出了一种将本模型与现有偏见类型分类器相结合的流程。尽管受限于句子级分析及因缺乏更大型先进偏见语料库而导致的数据集规模约束，我们的方法仍展现出良好的泛化能力和可解释性。我们探讨了上下文感知建模、偏见中和及高级偏见类型分类等潜在未来研究方向。本研究为构建更鲁棒、可解释且符合社会责任的媒体偏见检测自然语言处理系统提供了有益参考。