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Prevalence of Cognitive Distortion Markers in a Suicide Prevention Chat Service: Mixed Methods Study
Marijn Ten Thij1,2, Saskia Mérelle3, Renske Gilissen3,4
1Department of Intelligent Systems, Tilburg School of Humanities and Digital Sciences, Tilburg University, Warandelaan 2, Tilburg, 5037 AB, The Netherlands.
JMIR Mental Health
|April 28, 2026
Summary
Individuals seeking help via suicide helplines exhibit significantly more cognitive distortions in their language than counselors. Identifying these language patterns can improve online mental health interventions.
Area of Science:
- Psychology
- Computational Linguistics
- Digital Mental Health
Background:
- Suicide helplines are increasingly using chat services for immediate support.
- The effectiveness of text-based communication in crisis intervention is influenced by language and structure.
Purpose of the Study:
- To investigate the prevalence of cognitive distortions in the language of individuals seeking help from the Dutch 113 suicide helpline.
- To compare language patterns of help-seekers with control groups to understand suicidality markers.
Main Methods:
- Natural language processing (NLP) was used to analyze a large dataset of 71,148 chat sessions from the Dutch 113 suicide helpline.
- Prevalence of cognitive distortions was assessed in both help-seekers and counselors.
- Analysis included comparison with large Dutch social media and web text corpora.
Main Results:
- Nearly all types of cognitive distortions were more prevalent in help-seekers' language compared to helpline counselors.
- Personalizing (20.22x), emotional reasoning (7.87x), and mental filtering (4.53x) distortions were significantly higher among help-seekers.
- A distinct linguistic pattern associated with suicidality was identified in help-seekers.
Conclusions:
- The findings suggest that lexical analysis can detect cognitive and linguistic markers of suicidality.
- This research opens avenues for enhancing online therapeutic interventions by targeting specific cognitive distortions.
- Improved detection of cognitive distortions can lead to more effective digital mental health support.
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