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<p>Rumors on social media can cause panic during emergency events. Unlike conventional rumor detection, it is more challenging to detect rumors about emergency events that have not happened in history, due to the shortage of relevant corpus. Therefore, we treat emergency events rumor detection as a few-shot learning problem. Recently, large language models (LLMs) such as ChatGPT have been widely considered in various NLP tasks. However, LLMs may face limitations due to non-real-time knowledge and unwillingness to provide direct answers. Prompt learning effectively leverages pre-trained language models (PLMs), and prompt tuning can inject the latest knowledge into PLMs with very few instances. Therefore, we propose a template-based Ensembled Prompt Tuning (EPT) model. We contribute an approach leveraging the knowledge in PLMs to generate label words for verbalizer construction. Furthermore, we treat few-shot rumor detection as an MLM problem and design two types of prompt templates for online posts and comments. An ensemble strategy is introduced to make the final prediction. Experimental results on three datasets demonstrate the effectiveness of the proposed EPT model, outperforming current SOTA on accuracy and F1-score. Ablation study has shown the necessity and effectiveness of the ensemble strategy. Besides, we make comparisons with prevalent LLMs under both few-shot and zero-shot settings, the results show the competitiveness of our EPT model.</p>

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Rumor detection for emergency events via few-shot ensembled prompt learning

  • 期刊论文

  • 发表日期：

    2025年5月8日

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  • Chen Su,
  • Junkang Zhou,
  • Zhentao Jiang,
  • Shuwei Zhu,
  • Chao Li,
  • Wei Fang,
  • Heng-yang Lu

摘要

Rumors on social media can cause panic during emergency events. Unlike conventional rumor detection, it is more challenging to detect rumors about emergency events that have not happened in history, due to the shortage of relevant corpus. Therefore, we treat emergency events rumor detection as a few-shot learning problem. Recently, large language models (LLMs) such as ChatGPT have been widely considered in various NLP tasks. However, LLMs may face limitations due to non-real-time knowledge and unwillingness to provide direct answers. Prompt learning effectively leverages pre-trained language models (PLMs), and prompt tuning can inject the latest knowledge into PLMs with very few instances. Therefore, we propose a template-based Ensembled Prompt Tuning (EPT) model. We contribute an approach leveraging the knowledge in PLMs to generate label words for verbalizer construction. Furthermore, we treat few-shot rumor detection as an MLM problem and design two types of prompt templates for online posts and comments. An ensemble strategy is introduced to make the final prediction. Experimental results on three datasets demonstrate the effectiveness of the proposed EPT model, outperforming current SOTA on accuracy and F1-score. Ablation study has shown the necessity and effectiveness of the ensemble strategy. Besides, we make comparisons with prevalent LLMs under both few-shot and zero-shot settings, the results show the competitiveness of our EPT model.