C 2 D 2 Dataset: A Resource for the Cognitive Distortion Analysis and Its Impact on Mental Health (2023)

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 C hinese C ognitive D istortion 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.

## introduction cognitive distortions are irrational thinking patterns that can lead to distorted perceptions of reality @xcite . for example, the thought "i lost my puppy, and my future will be perpetually filled with sadness and loneliness" exemplifies a cognitive distortion known as "overgeneralization."in this case, the conclusion implies an excessively broad and permanent state of unhappiness. a more precise and less distorted thought for this situation would be: "the sudden loss of my puppy was incredibly painful, and i struggled to come to terms with it." as depicted in figure 1 , cognitive distortions significantly hinder individuals' perception, trapping them further through continuous self-reinforcement and contributing to the development of mental disorders, including depression, anxiety, and postcognitive distortions are constantly strengthened within this reinforcing loop, contributing to the development of mental disorders such as depression, ptsd, and anxiety @xcite . to tackle these challenges, we introduce our c2d2 dataset, a chinese dataset created to address the shortage of research resources regarding cognitive distortions. it is a publicly accessible resource within this domain that includes 7,500 instances of cognitive distortion thoughts from 450 different scenes. the research of our c2d2 can promote research on cognitive distortion and provide insights into users' mental state. we believe this resource will contribute to mental health research in china, a developing country, particularly in light of increasing social pressure and inadequate support for the mental healthcare system. moreover, we conduct various experiments, including the cognitive distortion detection and exploring the relationship between cognitive distortions and mental disorders. we compare the performance of finetuning different pretrained models and large language models using in-context learning on c2d2 dataset. we demonstrate the current models' ability to detect cognitive distortions. furthermore, our research is not limited to cognitive distortions alone. inspired by psychology, we use computational methods to investigate the association between cognitive distortions and mental health. we use several datasets on mental health to detect and analyze present cognitive distortions in the online texts of individuals with different disorders using our model. we discover some interesting phenomena and conclusions that have not been previously considered, but could potentially validate the underlying mechanisms of certain disorders. finally, we develop a simple method to enhance the performance of detecting disorders based on cognitive distortions. the creation of the c2d2 dataset contributes to a better understanding of cognitive distortions and the impact of cognitive distortions on mental health. ## related work in the following sections, we review studies pertaining to cognitive distortions, covering both psychological research and computational techniques. ## dataset construction previous researchers have collected posts from social media platforms or gathered data related to cognitive distortions through crowdsourced writing @xcite . we have chosen not to directly annotate social media content due to the inability to effectively control its quality and accuracy. instead, we have adopted a specially designed task to collect cognitive distortion thoughts. unlike previous work that recruit volunteers widely from the internet, our data annotation process is executed through a collaborative effort between carefully selected and specifically trained volunteers and domain experts. as depicted in figure 2 , we will further describe our task design and the three phases, which include volunteer recruitment and screening, data annotation, and expert evaluation. ## cognitive distortion detection we develop a model to detect cognitive distortions in text and assign specific categories to these cognitive distortions. this detection and categorization can facilitate interventions by psychologists and further analysis for treatment purposes. the task, cognitive distortion detection, involves inputting text x containing cognitive distortions and generating a multi-class label @xmath0, 7}. here, 0 denotes non-distorted, while the other values represent distinct categories of cognitive distortions. ## cognitive distortion for mental health after constructing the cognitive distortion detection task, we aim to introduce the importance of cognitive distortions in mental health. previous computational research has concentrated on examining the emotions and symptoms exhibited by individuals with mental disorders. in contrast, our objective is to find out the underlying cognitive distortion. this section sims to examine two fundamental questions in order to underscore the significance of cognitive distortion detection. q1: how do thinking patterns differ between individuals diagnosed with mental disorders and the normal group? in section 6.2, we employ our cognitive distortion detection model to analyze social media posts from individuals diagnosed with depression and ptsd, as well as normal group without any self-reported mental health issues. from a cognitive psychology perspective, we aim to identify the cognitive distortions to detect differences between these groups. q2: can these differences be utilized for mental disorder detection? in section 6.3, we simply integrate the cognitive distortions into the method for detecting mental disorders, with a specific focus on depression and ptsd. this approach underscores the potential utility of cognitive distortion to improve mental disorder detection, particularly when conventional research largely concentrates on symptoms and emotional manifestations. ## conclusion we have introduced the c2d2 dataset, which is the first public chinese dataset focused on cognitive distortions. this dataset illuminates a profound connection between cognitive distortions and mental disorders. addressing these distortions could potentially enhance mental health interventions. by integrating cognitive distortions as an additional feature, we have augmented the performance of existing mental disorder detection models. the introduction of the c2d2 dataset represents a valuable contribution to computational psychology and serves as a catalyst for further research in this emerging field.