arXiv:2605.24996v1 Announce Type: new Abstract: Cognitive distortions, distorted patterns of thinking, have been increasingly studied in computational mental health research. Although they are related to many, if not all, mental health disorders, most existing studies focus primarily on depression. In this work, we explore distortion profiles across multiple mental health conditions. We analyzed a large Reddit-based dataset containing posts from nine self-reported mental health groups as well as a control group using both an n-gram-based method and a fine-tuned transformer model for detecting
The increasing availability of large text datasets and advancements in transformer models enable more sophisticated computational analyses of complex human behaviors like cognitive distortions.
This research provides deeper insights into the nuanced manifestation of cognitive distortions across various mental health conditions, moving beyond a singular focus on depression.
The ability to identify distinct 'distortion profiles' opens new avenues for personalized mental health diagnostics and interventions, moving towards more targeted computational approaches.
- · Computational psychiatry researchers
- · Mental health tech startups
- · Psychological therapy providers
- · Generic mental health screening tools
Improved computational models for detecting and classifying different mental health disorders based on linguistic patterns.
Development of AI-powered tools that can identify specific cognitive distortions in individuals, potentially informing therapeutic strategies.
A future where personalized, AI-driven mental health support systems could offer real-time feedback and interventions based on an individual's unique distortion profile.
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