Methods in predictive techniques for mental health status on social media: a critical review
Abstract Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research pr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2020-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-020-0233-7 |
| _version_ | 1797430135655235584 |
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| author | Stevie Chancellor Munmun De Choudhury |
| author_facet | Stevie Chancellor Munmun De Choudhury |
| author_sort | Stevie Chancellor |
| collection | DOAJ |
| description | Abstract Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space. |
| first_indexed | 2024-03-09T09:24:15Z |
| format | Article |
| id | doaj.art-9ec6ccee175443fb87125d4dbc61ab45 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-09T09:24:15Z |
| publishDate | 2020-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-9ec6ccee175443fb87125d4dbc61ab452023-12-02T06:48:02ZengNature Portfolionpj Digital Medicine2398-63522020-03-013111110.1038/s41746-020-0233-7Methods in predictive techniques for mental health status on social media: a critical reviewStevie Chancellor0Munmun De Choudhury1Department of Computer Science, Northwestern UniversitySchool of Interactive Computing, Georgia TechAbstract Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space.https://doi.org/10.1038/s41746-020-0233-7 |
| spellingShingle | Stevie Chancellor Munmun De Choudhury Methods in predictive techniques for mental health status on social media: a critical review npj Digital Medicine |
| title | Methods in predictive techniques for mental health status on social media: a critical review |
| title_full | Methods in predictive techniques for mental health status on social media: a critical review |
| title_fullStr | Methods in predictive techniques for mental health status on social media: a critical review |
| title_full_unstemmed | Methods in predictive techniques for mental health status on social media: a critical review |
| title_short | Methods in predictive techniques for mental health status on social media: a critical review |
| title_sort | methods in predictive techniques for mental health status on social media a critical review |
| url | https://doi.org/10.1038/s41746-020-0233-7 |
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