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EMNLP Findings2023

C2D2 Dataset: A Resource for the Cognitive Distortion Analysis and Its Impact on Mental Health

Bichen Wang, Pengfei Deng, Yanyan Zhao, Bing Qin

Abstract

Cognitive distortions refer to patterns of irrational thinking that can lead to distorted perceptions of reality and mental health problems in individuals. Despite previous attempts to detect cognitive distortion through language, progress has been slow due to the lack of appropriate data. In this paper, we present the C2D2 dataset, the first expert-supervised Chinese Cognitive Distortion Dataset, which contains 7,500 cognitive distortion thoughts in everyday life scenes. Additionally, we examine the presence of cognitive distortions in social media texts shared by individuals diagnosed with mental disorders, providing insights into the association between cognitive distortions and mental health conditions. We propose that incorporating information about users' cognitive distortions can enhance the performance of existing models mental disorder detection. We contribute to a better understanding of how cognitive distortions appear in individuals' language and their impact on mental health.

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Cited by top-tier papers1

  1. Responsible Evaluation of AI for Mental HealthHiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen Eberhardt et al.ACL 2026

Builds on3

  1. Training language models to follow instructions with human feedbackLong Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida et al.NeurIPS 2022 · 24,707 citations
  2. Revisiting Pre-Trained Models for Chinese Natural Language ProcessingYiming Cui, Wanxiang Che, Ting Liu, Bing Qin et al.EMNLP Findings 2020 · 866 citations
  3. Inducing Positive Perspectives with Text ReframingCaleb Ziems, Minzhi Li, Anthony Zhang, Diyi YangACL 2022 · 46 citations

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