Academic

CoALFake: Collaborative Active Learning with Human-LLM Co-Annotation for Cross-Domain Fake News Detection

arXiv:2604.04174v1 Announce Type: new Abstract: The proliferation of fake news across diverse domains highlights critical limitations in current detection systems, which often exhibit narrow domain specificity and poor generalization. Existing cross-domain approaches face two key challenges: (1) reliance on labelled data, which is frequently unavailable and resource intensive to acquire and (2) information loss caused by rigid domain categorization or neglect of domain-specific features. To address these issues, we propose CoALFake, a novel approach for cross-domain fake news detection that integrates Human-Large Language Model (LLM) co-annotation with domain-aware Active Learning (AL). Our method employs LLMs for scalable, low-cost annotation while maintaining human oversight to ensure label reliability. By integrating domain embedding techniques, the CoALFake dynamically captures both domain specific nuances and cross-domain patterns, enabling the training of a domain agnostic model

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Esma A\"imeur, Gilles Brassard, Dorsaf Sallami · · 1 min read · 2 views
#cs.AI

arXiv:2604.04174v1 Announce Type: new Abstract: The proliferation of fake news across diverse domains highlights critical limitations in current detection systems, which often exhibit narrow domain specificity and poor generalization. Existing cross-domain approaches face two key challenges: (1) reliance on labelled data, which is frequently unavailable and resource intensive to acquire and (2) information loss caused by rigid domain categorization or neglect of domain-specific features. To address these issues, we propose CoALFake, a novel approach for cross-domain fake news detection that integrates Human-Large Language Model (LLM) co-annotation with domain-aware Active Learning (AL). Our method employs LLMs for scalable, low-cost annotation while maintaining human oversight to ensure label reliability. By integrating domain embedding techniques, the CoALFake dynamically captures both domain specific nuances and cross-domain patterns, enabling the training of a domain agnostic model. Furthermore, a domain-aware sampling strategy optimizes sample acquisition by prioritizing diverse domain coverage. Experimental results across multiple datasets demonstrate that the proposed approach consistently outperforms various baselines. Our results emphasize that human-LLM co-annotation is a highly cost-effective approach that delivers excellent performance. Evaluations across several datasets show that CoALFake consistently outperforms a range of existing baselines, even with minimal human oversight.

Executive Summary

The article introduces CoALFake, a novel framework for cross-domain fake news detection that addresses critical limitations in existing systems, such as domain specificity and poor generalization. By integrating Human-Large Language Model (LLM) co-annotation with domain-aware Active Learning (AL), CoALFake leverages scalable LLM annotations while maintaining human oversight for reliability. The approach employs domain embeddings to capture both domain-specific nuances and cross-domain patterns, enabling training of a domain-agnostic model. A domain-aware sampling strategy prioritizes diverse domain coverage, optimizing sample acquisition. Experimental results across multiple datasets demonstrate that CoALFake outperforms existing baselines, even with minimal human oversight, highlighting its cost-effectiveness and superior performance in cross-domain fake news detection.

Key Points

  • ▸ Introduces CoALFake, a cross-domain fake news detection framework leveraging Human-LLM co-annotation and domain-aware Active Learning (AL).
  • ▸ Combines scalable LLM annotations with human oversight to ensure label reliability while reducing resource-intensive manual labeling.
  • ▸ Utilizes domain embeddings and a domain-aware sampling strategy to dynamically capture domain-specific nuances and cross-domain patterns, enabling training of a domain-agnostic model.
  • ▸ Demonstrates consistent performance improvements over existing baselines across multiple datasets, even with minimal human intervention.

Merits

Innovative Integration of Human-LLM Co-Annotation and Active Learning

The novel combination of Human-LLM co-annotation with domain-aware Active Learning addresses the critical challenge of labeled data scarcity while maintaining annotation reliability and reducing costs.

Dynamic Domain Adaptation

The use of domain embeddings and a domain-aware sampling strategy enables the model to dynamically capture both domain-specific nuances and cross-domain patterns, overcoming the limitations of rigid domain categorization.

Empirical Superiority

Experimental results across multiple datasets demonstrate that CoALFake consistently outperforms a range of existing baselines, even with minimal human oversight, underscoring its efficacy and cost-effectiveness.

Demerits

Dependence on LLM Quality and Human Oversight

The framework's performance is contingent on the quality of LLM annotations and the reliability of human oversight, which may introduce variability or biases if not properly managed.

Limited Generalizability to Non-Textual Modalities

While the approach is effective for text-based fake news detection, its applicability to non-textual modalities (e.g., images, videos) remains unaddressed, limiting its scope in multimedia disinformation contexts.

Computational Complexity

The integration of domain embeddings and active learning strategies may increase computational overhead, potentially posing challenges for deployment in resource-constrained environments.

Expert Commentary

The CoALFake framework represents a significant advancement in cross-domain fake news detection by addressing two critical limitations in existing systems: the scarcity of labeled data and poor generalization across domains. The integration of Human-LLM co-annotation with domain-aware Active Learning is particularly innovative, as it leverages the scalability of LLMs while mitigating their reliability concerns through human oversight. The use of domain embeddings to dynamically capture domain-specific and cross-domain patterns is a notable contribution, enabling the training of a domain-agnostic model that generalizes more effectively. However, the framework's reliance on LLM quality and human oversight introduces potential variability, and its applicability to non-textual modalities remains an open question. Additionally, the computational complexity of the approach may pose challenges for deployment in real-time or resource-constrained environments. Despite these limitations, CoALFake offers a promising pathway for more robust and scalable fake news detection, with implications for both practical applications and policy development. Further research is warranted to explore its adaptability to multimodal disinformation and to assess its performance in real-world, high-stakes scenarios.

Recommendations

  • ✓ Expand the framework to incorporate multimodal disinformation detection, integrating text, images, videos, and audio to address the evolving landscape of disinformation.
  • ✓ Develop robust bias mitigation strategies to ensure the reliability and fairness of LLM and human annotations, including regular audits and transparent reporting of annotation processes.
  • ✓ Optimize the computational efficiency of CoALFake to enhance scalability and real-time detection capabilities, potentially through algorithmic improvements or hardware acceleration.
  • ✓ Conduct longitudinal studies to evaluate the long-term performance and adaptability of CoALFake in dynamic, real-world environments, including its resilience to adversarial attacks or evolving disinformation tactics.
  • ✓ Engage with policymakers and legal experts to establish ethical and regulatory frameworks for the deployment of AI-driven fake news detection systems, ensuring compliance with emerging regulations and alignment with public interest.

Sources

Original: arXiv - cs.AI

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