@inproceedings{wang-etal-2023-c2d2,
title = "{C}2{D}2 Dataset: A Resource for the Cognitive Distortion Analysis and Its Impact on Mental Health",
author = "Wang, Bichen and
Deng, Pengfei and
Zhao, Yanyan and
Qin, Bing",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.680",
doi = "10.18653/v1/2023.findings-emnlp.680",
pages = "10149--10160",
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 \textbf{C}hinese \textbf{C}ognitive \textbf{D}istortion \textbf{D}ataset, 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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<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.</abstract>
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%0 Conference Proceedings
%T C2D2 Dataset: A Resource for the Cognitive Distortion Analysis and Its Impact on Mental Health
%A Wang, Bichen
%A Deng, Pengfei
%A Zhao, Yanyan
%A Qin, Bing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-c2d2
%X 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.
%R 10.18653/v1/2023.findings-emnlp.680
%U https://aclanthology.org/2023.findings-emnlp.680
%U https://doi.org/10.18653/v1/2023.findings-emnlp.680
%P 10149-10160
Markdown (Informal)
[C2D2 Dataset: A Resource for the Cognitive Distortion Analysis and Its Impact on Mental Health](https://aclanthology.org/2023.findings-emnlp.680) (Wang et al., Findings 2023)
ACL