Who are Frequent Chatters? Characterization of Frequent Users in a 24/7 Messenger-Based Psychological Chat Counseling Service for Children and Adolescents

Efe, Zeki; Baldofski, Sabrina; Eckert, Melanie; Guenthner, Lukas; Saee, Shadi; Thomas, Julia Adeney; Wundrack, Richard; Kohls, Elisabeth; Rummel‐Kluge, Christine

doi:10.2139/ssrn.4331991

Cited by 2 publications

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“…This means there may be some further unmet need that we cannot address with our outcome. However, past evaluation efforts showed that around 2⁄3 of the chatters reported high satisfaction and nearly 90% a high likelihood to recommend the service 32 , suggesting that this may have been the case only for very few chatters.…”

Section: Discussionmentioning

confidence: 99%

“…Here, recurrent chat contact has been described as a major pain point of the service providers. Chat capabilities are limited, and heavy users have been shown to block a significant share of counselors' time 32 . This threatens the ability of the services to provide counseling to all incoming help seekers, leaving those contacting possibly without any support.…”

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confidence: 99%

“…Also, it remains questionable whether recurrent chat consultations would be the most adequate support for those in need of considerable additional help. Frequent chatters have been shown to have significantly more severe problems 32 . Similar insights have been reported for other low-threshold interventions like telephone helplines 33 .…”

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confidence: 99%

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Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing

Hornstein,

Scharfenberger,

Lueken

et al. 2024

npj Digit. Med.

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Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health. However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing (NLP) applications within this setting. Therefore, this preregistered approach (OSF: XA4PN) utilizes a sample of approximately 19,000 children and young adults that received a chat consultation from a 24/7 crisis service in Germany. Around 800,000 messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross-validation and bayesian optimization for hyperparameter search. The best model was able to achieve an AUROC score of 0.68 (p < 0.01) on the previously unseen 3942 newest consultations. A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self-harm and suicidal thoughts were associated with a higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path toward personalized care at chat hotlines.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing

Hornstein,

Scharfenberger,

Lueken

et al. 2024

npj Digit. Med.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, recurrence chat contact has been described as a major pain point to the service providers. Chat capabilities are limited, and heavy users have been shown to block a significant share of counselors' time 34 . This threatens the ability of the services to provide counseling to all incoming help seekers.…”

Section: Introductionmentioning

confidence: 99%

Predicting Recurrent Chat Contact in a Low-Threshold Psychological Intervention for Children and Young Adults: A Natural Language Processing Approach

Hornstein,

Scharfenberger,

Lueken

et al. 2023

Preprint

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Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health. However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing (NLP) applications within this setting. Therefore, this preregistered approach (OSF: XA4PN) utilizes a sample of approximately 19,000 children and young adults that received a chat consultation from a 24/7 crisis service in Germany. Around 800,000 messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross validation and bayesian optimization for hyperparameter search. The best model was able to achieve an AUROC score of 0.68 (p < .01) on the previously unseen 3,942 newest consultations. A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self harm and suicidal thoughts were associated with higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path towards personalized care at chat hotlines.

show abstract

Who are Frequent Chatters? Characterization of Frequent Users in a 24/7 Messenger-Based Psychological Chat Counseling Service for Children and Adolescents

Cited by 2 publications

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Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing

Predicting recurrent chat contact in a psychological intervention for the youth using natural language processing

Predicting Recurrent Chat Contact in a Low-Threshold Psychological Intervention for Children and Young Adults: A Natural Language Processing Approach

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