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This article introduces the CoALFake framework, which combines human-machine collaborative annotation with domain-aware active learning to address the problems of scarce labeled data and loss of domain features in cross-domain fake news detection, enabling efficient and accurate fake news identification.
Section 01
This article proposes the CoALFake framework, which combines human-machine collaborative annotation with domain-aware active learning to solve the core problems of scarce labeled data and loss of domain features in cross-domain fake news detection, achieving efficient and accurate fake news identification.
Section 02
Section 03
LLMs are responsible for initial annotation (scalable, low-cost), while human experts supervise and validate, balancing cost-effectiveness, quality assurance, and scalability.
Dynamically captures domain-specific details and cross-domain patterns, helping to train truly domain-agnostic detection models.
Active learning prioritizes samples with diverse domain coverage, avoiding over-representation or under-representation of certain domains and improving generalization ability.
Section 04
Performs excellently on multiple datasets with strong generalization ability, adapting to different data distributions and domain characteristics.
Even with minimal human supervision, it maintains excellent performance and effectively utilizes limited expert resources.
Significantly improves key metrics such as accuracy, recall, and F1 score, thanks to the effective use of domain information and intelligent management of the annotation process.
Section 05
Helps news agencies and fact-checking organizations prioritize the identification of high-risk fake news and optimize resource allocation.
Its cross-domain capability and cost-effectiveness make it a potential solution for platform-level detection, balancing freedom of speech and fake information containment.
Provides a reference for the idea of combining human-machine collaboration and active learning, which can be extended to other NLP tasks requiring large-scale annotation.
Section 06
The current framework mainly targets English news and does not support multilingualism; inference efficiency needs to be optimized for real-time detection; adversarial robustness needs to be improved to deal with malicious evasion.
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