With advancements in natural language processing (NLP) models, automatic
explanation generation has been proposed to mitigate misinformation on social
media platforms in addition to adding warning labels to identified fake news.
While many researchers have focused on generating good explanations, how these
explanations can really help humans combat fake news is under-explored. In this
study, we compare the effectiveness of a warning label and the state-of-the-art
counterfactual explanations generated by GPT-4 in debunking misinformation. In
a two-wave, online human-subject study, participants (N = 215) were randomly
assigned to a control group in which false contents are shown without any
intervention, a warning tag group in which the false claims were labeled, or an
explanation group in which the false contents were accompanied by GPT-4
generated explanations. Our results show that both interventions significantly
decrease participants' self-reported belief in fake claims in an equivalent
manner for the short-term and long-term. We discuss the implications of our
findings and directions for future NLP-based misinformation debunking
strategies.

本研究比较了使用警告标签和由 GPT-4 生成的最新对抗解释来揭穿虚假信息的效果，结果表明这两种干预方法在短期和长期内都能显著减少参与者对虚假主张的自我报告信任，进一步讨论了这些发现的意义和未来基于自然语言处理的虚假信息揭穿策略的发展方向。