Novel application of deep learning to evaluate conversations from a mental health text support service
The Shout text support service supports individuals experiencing mental health distress through anonymous text conversations. As one of the first research projects on the Shout dataset and one of the first significant attempts to apply advanced deep learning to a text messaging service, this project...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Natural Language Processing Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949719124000670 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846123227059847168 |
|---|---|
| author | Daniel Cahn Sarah Yeoh Lakshya Soni Ariele Noble Mark A. Ungless Emma Lawrance Ovidiu Şerban |
| author_facet | Daniel Cahn Sarah Yeoh Lakshya Soni Ariele Noble Mark A. Ungless Emma Lawrance Ovidiu Şerban |
| author_sort | Daniel Cahn |
| collection | DOAJ |
| description | The Shout text support service supports individuals experiencing mental health distress through anonymous text conversations. As one of the first research projects on the Shout dataset and one of the first significant attempts to apply advanced deep learning to a text messaging service, this project is a proof-of-concept demonstrating the potential of using deep learning to text messages. Several areas of interest to Shout are identifying texter characteristics, emphasising high suicide-risk participants, and understanding what can make conversations helpful to texters. Therefore, from a mental health perspective, we look at (1) characterising texter demographics strictly based on the vocabulary used throughout the conversation, (2) predicting an individual’s risk of suicide or self-harm, and (3) assessing conversation success by developing robust outcome metrics. To fulfil these aims, a series of Machine Learning models were trained using data from post-conversation surveys to predict the different levels of suicide risk, whether a conversation was helpful, and texter characteristics, such as demographic information. The results show that language models based on Deep Learning significantly improve understanding of this highly subjective dataset. We compare traditional methods and basic meta-features with the latest developments in Transformer-based architectures and showcase the advantages of mental health research. |
| format | Article |
| id | doaj-art-e6f5fe5048ea496d9071a2ca23e974d0 |
| institution | Kabale University |
| issn | 2949-7191 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Natural Language Processing Journal |
| spelling | doaj-art-e6f5fe5048ea496d9071a2ca23e974d02024-12-14T06:34:35ZengElsevierNatural Language Processing Journal2949-71912024-12-019100119Novel application of deep learning to evaluate conversations from a mental health text support serviceDaniel Cahn0Sarah Yeoh1Lakshya Soni2Ariele Noble3Mark A. Ungless4Emma Lawrance5Ovidiu Şerban6Department of Computing, Imperial College London, South Kensington Campus, London SW7 2AZ, UKDepartment of Computing, Imperial College London, South Kensington Campus, London SW7 2AZ, UKDepartment of Surgery & Cancer, Imperial College London, St. Mary’s Campus, London W2 1NY, UK; Institute of Global Health Innovation, Imperial College London, South Kensington Campus, London SW7 2AZ, UKMental Health Innovations, London, UKMental Health Innovations, London, UKInstitute of Global Health Innovation, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; Mental Health Innovations, London, UKData Science Institute, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; Department of Computing, Imperial College London, South Kensington Campus, London SW7 2AZ, UK; Corresponding author at: Data Science Institute, Imperial College London, South Kensington Campus, London SW7 2AZ, UK.The Shout text support service supports individuals experiencing mental health distress through anonymous text conversations. As one of the first research projects on the Shout dataset and one of the first significant attempts to apply advanced deep learning to a text messaging service, this project is a proof-of-concept demonstrating the potential of using deep learning to text messages. Several areas of interest to Shout are identifying texter characteristics, emphasising high suicide-risk participants, and understanding what can make conversations helpful to texters. Therefore, from a mental health perspective, we look at (1) characterising texter demographics strictly based on the vocabulary used throughout the conversation, (2) predicting an individual’s risk of suicide or self-harm, and (3) assessing conversation success by developing robust outcome metrics. To fulfil these aims, a series of Machine Learning models were trained using data from post-conversation surveys to predict the different levels of suicide risk, whether a conversation was helpful, and texter characteristics, such as demographic information. The results show that language models based on Deep Learning significantly improve understanding of this highly subjective dataset. We compare traditional methods and basic meta-features with the latest developments in Transformer-based architectures and showcase the advantages of mental health research.http://www.sciencedirect.com/science/article/pii/S2949719124000670Applied mental health researchUnderstanding user conversationsTransformer-based architectures |
| spellingShingle | Daniel Cahn Sarah Yeoh Lakshya Soni Ariele Noble Mark A. Ungless Emma Lawrance Ovidiu Şerban Novel application of deep learning to evaluate conversations from a mental health text support service Natural Language Processing Journal Applied mental health research Understanding user conversations Transformer-based architectures |
| title | Novel application of deep learning to evaluate conversations from a mental health text support service |
| title_full | Novel application of deep learning to evaluate conversations from a mental health text support service |
| title_fullStr | Novel application of deep learning to evaluate conversations from a mental health text support service |
| title_full_unstemmed | Novel application of deep learning to evaluate conversations from a mental health text support service |
| title_short | Novel application of deep learning to evaluate conversations from a mental health text support service |
| title_sort | novel application of deep learning to evaluate conversations from a mental health text support service |
| topic | Applied mental health research Understanding user conversations Transformer-based architectures |
| url | http://www.sciencedirect.com/science/article/pii/S2949719124000670 |
| work_keys_str_mv | AT danielcahn novelapplicationofdeeplearningtoevaluateconversationsfromamentalhealthtextsupportservice AT sarahyeoh novelapplicationofdeeplearningtoevaluateconversationsfromamentalhealthtextsupportservice AT lakshyasoni novelapplicationofdeeplearningtoevaluateconversationsfromamentalhealthtextsupportservice AT arielenoble novelapplicationofdeeplearningtoevaluateconversationsfromamentalhealthtextsupportservice AT markaungless novelapplicationofdeeplearningtoevaluateconversationsfromamentalhealthtextsupportservice AT emmalawrance novelapplicationofdeeplearningtoevaluateconversationsfromamentalhealthtextsupportservice AT ovidiuserban novelapplicationofdeeplearningtoevaluateconversationsfromamentalhealthtextsupportservice |