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The paper introduces ContentFuzz, a framework that uses LLMs to rewrite social media posts to alter their machine-inferred stance, thereby escaping information cocoons. It leverages confidence scores from stance detection models to guide meaning-preserving rewrites. Experiments across four stance detection models, three datasets, and two languages demonstrate ContentFuzz's ability to change stance labels while preserving semantic integrity.
Rewrite your social media posts to bypass echo chambers: this method fools stance detection models into routing your content to new audiences.
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.
