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.

Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term

Thematic analysis (TA) has been widely used
for analyzing qualitative data in many disciplines and fields. To ensure reliable analysis,
the same piece of data is typically assigned to at least two human coders. Moreover, to produce
meaningful and useful analysis, human coders
develop and deepen their data interpretation
and coding over multiple iterations, making TA
labor-intensive and time-consuming. Recently
the emerging field of large language models
(LLMs) research has shown that LLMs have
the potential replicate human-like behavior in
various tasks: in particular, LLMs outperform
crowd workers on text-annotation tasks, suggesting an opportunity to leverage LLMs on
TA. We propose a human–LLM collaboration
framework (i.e., LLM-in-the-loop) to conduct
TA with in-context learning (ICL). This framework provides the prompt to frame discussions
with a LLM (e.g., GPT-3.5) to generate the final
codebook for TA. We demonstrate the utility
of this framework using survey datasets on the
aspects of the music listening experience and
the usage of a password manager. Results of
the two case studies show that the proposed
framework yields similar coding quality to that
of human coders but reduces TA’s labor and
time demands.

LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis

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**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

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Aiping Xiong

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Is Explanation the Cure? Misinformation Mitigation in the Short Term and Long Term

LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysis

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