Is Explanation the Cure? Misinformation Mitigation in the Short Term and
  Long Term
Yi-Li Hsu Shih-Chieh Dai Aiping Xiong Lun-Wei Ku
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.