一种融合因果干预的新闻反事实去偏方法

News Counterfactual De-bias Method Integrating Causal Intervention

摘要

鉴于新闻偏见对公众认知、社会信任及公平性的深层影响,利用自然语言处理技术构建透明、可解释的去偏见框架,已成为传播学与人工智能的交叉研究热点.现有研究主要围绕两类偏见展开:词汇偏见和框架偏见.在词汇偏见方面,主流方法多通过词汇替换来消除文本中的显性偏见词,但仍存在中性词语中蕴含隐性立场倾向、上下文适应性差等问题;在框架偏见方面,现有研究多采用文本重构或多文本融合生成的方式来建模中立文本,但存在框架偏见不可观测、立场冲突难解耦、生成目标模糊等挑战,限制了偏见缓解效果的进一步提升.针对上述问题,本文提出一种融合因果干预与反事实推理的多阶段新闻偏见缓解方法.首先,针对词汇偏见,构建基于PMI的多立场偏见词典,并引入后门干预机制,通过语义相似度匹配进行词语替换,从而缓解显性偏见.其次,为应对结构性框架偏见的不可观测性,本文引入反事实推理方法,基于因果公式 TIE=TE-NDE 建模偏左与偏右框架对中立表达的影响,其中TE表示总偏见效应,NDE表示中立文本的自然直接效应,TIE则反映偏见传播的间接效应.最后,本文引入一个预训练的偏见检测器作为辅助监督模块,增强生成模型对文本中立性与专业性的建模能力.实验结果表明,本文方法在多个偏见缓解与文本质量评估指标上均显著优于现有主流方法,验证了该方法在多源新闻文本去偏任务中的有效性与实用价值.

Abstract

Given the profound impact of news bias on public perception,social trust,and fairness,building transparent and interpretable debiasing frameworks with natural language processing(NLP)has become a research focus at the intersection of communication studies and artificial intelligence.Existing work mainly targets lexical bias and framing bias.For lexical bias,mainstream approaches replace explicit biased words but struggle with implicit stance tendencies in neutral words and poor contextual adaptability.For framing bias,text reconstruction or multi-text fusion is often used,yet faces the unobservability of framing bias,difficulty in disentangling stance conflicts,and vague generation objectives,limiting further improvement.To address these issues,we propose a multi-stage news bias mitigation method combining causal intervention and counterfactual reasoning.A PMI-based multi-stance lexicon and a back-door intervention mechanism perform semantic similarity-based word replacement to reduce explicit bias.Counterfactual reasoning with TIE=TE-NDE models the influence of left-and right-leaning frames on neutral expressions,where TE is the total bias effect,NDE is the natural direct effect,and TIE captures indirect bias propagation.A pre-trained bias detector provides auxiliary supervision,enhancing the model′s ability to generate neutral and professional text.Experiments show our approach significantly outperforms mainstream methods across multiple debiasing and text quality metrics,confirming its effectiveness in multi-source news debiasing tasks.

关键词

新闻偏见 / 词汇偏见 / 框架偏见 / 因果干预 / 反事实推理

Key words

news bias / lexical bias / framing bias / causal inference / counterfactual reasoning

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基金资助

国家自然科学基金项目(62472170)资助.