Academic

Content Fuzzing for Escaping Information Cocoons on Digital Social Media

arXiv:2604.05461v1 Announce Type: new 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

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Yifeng He, Ziye Tang, Hao Chen · · 1 min read · 16 views
#cs.CL #cs.SI

arXiv:2604.05461v1 Announce Type: new 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.

Executive Summary

The article introduces ContentFuzz, a novel framework designed to mitigate the formation of 'information cocoons' on social media by enabling content creators to rewrite posts in a way that preserves their original intent while inducing different machine-inferred stance labels. Leveraging confidence-guided fuzzing and large language models (LLMs), ContentFuzz effectively alters the stance detection outcomes of posts across multiple models, datasets, and languages. The method seeks to broaden the dissemination of diverse viewpoints by routing content beyond existing affinity clusters, thereby fostering more inclusive and constructive discourse. The study demonstrates the framework's efficacy in maintaining semantic integrity while altering machine-classified stances, highlighting its potential to disrupt algorithmic reinforcement of ideological segregation.

Key Points

  • ▸ ContentFuzz employs a confidence-guided fuzzing approach to rewrite social media posts, preserving human-interpreted intent while altering machine-inferred stance labels.
  • ▸ Evaluated across four stance detection models, three datasets, and two languages, ContentFuzz demonstrates consistent effectiveness in changing stance classifications while maintaining semantic fidelity.
  • ▸ The framework introduces a creator-centric solution to address the 'information cocoon' problem, where algorithmic recommendation systems inadvertently limit users' exposure to diverse viewpoints.
  • ▸ ContentFuzz operationalizes stance detection models as feedback mechanisms to guide LLM-generated rewrites, enabling iterative refinement of post content.

Merits

Innovative Framework

ContentFuzz introduces a novel, automated approach to content modification that leverages machine learning feedback to achieve meaningful changes in stance detection outcomes, addressing a critical gap in existing literature on algorithmic bias and information segregation.

Empirical Rigor

The study demonstrates robust evaluation across multiple models, datasets, and languages, providing strong empirical evidence for the framework's effectiveness and generalizability.

Creator-Centric Solution

By focusing on the creator's ability to influence content dissemination, the framework offers a proactive solution to the 'information cocoon' problem, complementing existing reactive approaches such as content moderation or algorithmic transparency.

Preservation of Semantic Integrity

Despite altering machine-inferred stances, ContentFuzz ensures that the rewritten content remains semantically faithful to the original post, addressing concerns about misinformation or manipulation.

Demerits

Dependence on Stance Detection Models

The effectiveness of ContentFuzz is contingent on the accuracy and robustness of underlying stance detection models, which may themselves be subject to biases or errors that could propagate through the framework.

Limited User-Centric Validation

While the framework is evaluated from a technical standpoint, there is limited exploration of user-perceived intent preservation or the potential psychological impact of exposure to diverse viewpoints on social media users.

Scalability and Real-World Deployment

The practical deployment of ContentFuzz in real-world social media ecosystems may face challenges related to scalability, computational cost, and integration with existing platform architectures.

Ethical and Normative Concerns

The framework raises ethical questions about the intentional manipulation of algorithmic outputs, particularly in contexts where users or platforms may have competing objectives, such as promoting specific narratives or suppressing harmful content.

Expert Commentary

The authors present a compelling and timely solution to the pervasive issue of 'information cocoons' on social media, a phenomenon that has profound implications for democratic discourse and societal cohesion. ContentFuzz is particularly noteworthy for its creator-centric approach, which shifts the locus of control from opaque algorithmic systems to the content creators themselves. This is a significant departure from traditional interventions that focus on platform-level changes or user education. The use of confidence-guided fuzzing to iteratively refine post content is both innovative and technically sophisticated, demonstrating the potential of LLMs not just as generative tools but as instruments for altering machine-perceived biases. However, the framework's reliance on stance detection models introduces a critical vulnerability: if these models are themselves biased or flawed, the effectiveness of ContentFuzz could be compromised. Moreover, the ethical implications of intentionally altering algorithmic outputs warrant careful consideration, particularly in contexts where such alterations could be perceived as manipulation. The study's empirical rigor is commendable, but future work should explore the long-term social and psychological impacts of such tools on both creators and audiences. Overall, ContentFuzz represents a significant contribution to the field, offering a pragmatic yet nuanced approach to addressing algorithmic segregation while raising important questions about the future of content governance on digital platforms.

Recommendations

  • ✓ Future research should explore the integration of user feedback mechanisms to validate the preservation of intent and assess the perceived authenticity of rewritten content, ensuring that modifications align with both creator and audience expectations.
  • ✓ Platforms should conduct pilot studies to evaluate the real-world feasibility of deploying ContentFuzz-like tools, focusing on scalability, computational efficiency, and integration with existing recommendation systems.
  • ✓ Policymakers and platform governance bodies should develop frameworks for the ethical use of automated content modification tools, including guidelines for transparency, accountability, and the prevention of misuse in manipulative or deceptive contexts.
  • ✓ Researchers should investigate the potential for ContentFuzz to be adapted for other forms of algorithmic bias mitigation, such as gender or racial bias in content recommendation, broadening the framework's applicability.
  • ✓ Collaborative studies between computer scientists, social scientists, and ethicists are essential to assess the long-term societal impacts of such tools, ensuring that technical innovations align with broader goals of democratic discourse and social cohesion.

Sources

Original: arXiv - cs.CL

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