Content Fuzzing for Escaping Information Cocoons on Digital Social Media

• URL: http://arxiv.org/abs/2604.05461v1
• Date: Tue, 07 Apr 2026 05:49:58 GMT
• Title: Content Fuzzing for Escaping Information Cocoons on Digital Social Media
• Abstract summary: Information cocoons on social media limit users' exposure to posts with diverse viewpoints.<n>We present ContentFuzz, a confidence-guided fuzzing framework that rewrites posts while preserving their human-interpreted intent.<n>Our method guides a large language model to generate meaning-preserving rewrites using confidence feedback from stance detection models.
• Score: 5.7188898597157385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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