用智能改写让观点文章突破信息茧房，触达不同立场用户。
Content Fuzzing for Escaping Information Cocoons on Digital Social Media
- 基于立场检测模型的置信度反馈，引导大模型重写内容。
- 在多语言数据集上成功改变机器识别的立场标签。
- 适合希望扩大观点传播范围的内容创作者使用。
社交媒体中的信息茧房限制了用户接触多元观点。现代平台将立场识别作为推荐与排序的重要信号，导致内容主要流向同质化群体，削弱了跨立场交流。本文从内容创作者视角出发，提出ContentFuzz——一种置信度引导的模糊化框架，通过重写帖子，在保持人类可理解语义的同时，诱导机器识别出不同的立场标签，从而突破原有兴趣圈层。该方法利用立场检测模型的置信度反馈，指导大语言模型生成语义一致的改写版本。在三个数据集、四种立场检测模型及双语环境下的评估表明，ContentFuzz能有效改变机器判定的立场，同时保持原始内容的语义完整性。
原文摘要 · Abstract (English)
Information cocoons on social media limit users' exposure to posts with diverse viewpoints. Modern platforms use stance detection as an important signal in recommendation and ranking pipelines, which can route posts primarily to like-minded audiences and reduce cross-cutting exposure. This restricts the reach of dissenting opinions and hinders constructive discourse. We take the creator's perspective and investigate how content can be revised to reach beyond existing affinity clusters. We present ContentFuzz, a confidence-guided fuzzing framework that rewrites posts while preserving their human-interpreted intent and induces different machine-inferred stance labels. ContentFuzz aims to route posts beyond their original cocoons. Our method guides a large language model (LLM) to generate meaning-preserving rewrites using confidence feedback from stance detection models. Evaluated on four representative stance detection models across three datasets in two languages, ContentFuzz effectively changes machine-classified stance labels, while maintaining semantic integrity with respect to the original content.
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