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In this work we tackle the problem of the data quality and labeling in the machine learning task of detecting fake news and disinformation. The major contribution of this paper is the new proposition to use large language models as an additional annotation mechanism in order to enrich the dataset and model with the new information. In this manner, we are able to faster annotate new content, and limit so called aging effect of the models. Hereby, we also evaluate our approach and provide promising results.

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Evaluating the Strategy to Deploy Large Language Models to Label Training Data in the Process of Fake News Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Martyna Tarczewska,
  • Rafał Kozik,
  • Michał Choraś

摘要

In this work we tackle the problem of the data quality and labeling in the machine learning task of detecting fake news and disinformation. The major contribution of this paper is the new proposition to use large language models as an additional annotation mechanism in order to enrich the dataset and model with the new information. In this manner, we are able to faster annotate new content, and limit so called aging effect of the models. Hereby, we also evaluate our approach and provide promising results.