Methods and applications of social media monitoring of mental health during disasters : scoping review
- Teague, Samantha, Shatte, Adrian, Weller, Emmelyn, Fuller-Tyszkiewicz, Matthew, Hutchinson, Delyse
- Authors: Teague, Samantha , Shatte, Adrian , Weller, Emmelyn , Fuller-Tyszkiewicz, Matthew , Hutchinson, Delyse
- Date: 2022
- Type: Text , Journal article , Review
- Relation: JMIR Mental Health Vol. 9, no. 2 (2022), p.
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- Description: Background: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters. © 2022 Samantha J Teague, Adrian B R Shatte, Emmelyn Weller, Matthew Fuller-Tyszkiewicz, Delyse M Hutchinson.
- Authors: Teague, Samantha , Shatte, Adrian , Weller, Emmelyn , Fuller-Tyszkiewicz, Matthew , Hutchinson, Delyse
- Date: 2022
- Type: Text , Journal article , Review
- Relation: JMIR Mental Health Vol. 9, no. 2 (2022), p.
- Full Text:
- Reviewed:
- Description: Background: With the increasing frequency and magnitude of disasters internationally, there is growing research and clinical interest in the application of social media sites for disaster mental health surveillance. However, important questions remain regarding the extent to which unstructured social media data can be harnessed for clinically meaningful decision-making. Objective: This comprehensive scoping review synthesizes interdisciplinary literature with a particular focus on research methods and applications. Methods: A total of 6 health and computer science databases were searched for studies published before April 20, 2021, resulting in the identification of 47 studies. Included studies were published in peer-reviewed outlets and examined mental health during disasters or crises by using social media data. Results: Applications across 31 mental health issues were identified, which were grouped into the following three broader themes: estimating mental health burden, planning or evaluating interventions and policies, and knowledge discovery. Mental health assessments were completed by primarily using lexical dictionaries and human annotations. The analyses included a range of supervised and unsupervised machine learning, statistical modeling, and qualitative techniques. The overall reporting quality was poor, with key details such as the total number of users and data features often not being reported. Further, biases in sample selection and related limitations in generalizability were often overlooked. Conclusions: The application of social media monitoring has considerable potential for measuring mental health impacts on populations during disasters. Studies have primarily conceptualized mental health in broad terms, such as distress or negative affect, but greater focus is required on validating mental health assessments. There was little evidence for the clinical integration of social media-based disaster mental health monitoring, such as combining surveillance with social media-based interventions or developing and testing real-world disaster management tools. To address issues with study quality, a structured set of reporting guidelines is recommended to improve the methodological quality, replicability, and clinical relevance of future research on the social media monitoring of mental health during disasters. © 2022 Samantha J Teague, Adrian B R Shatte, Emmelyn Weller, Matthew Fuller-Tyszkiewicz, Delyse M Hutchinson.
Social media markers to identify fathers at risk of postpartum depression : a machine learning approach
- Shatte, Adrian, Hutchinson, Delyse, Fuller-Tyszkiewicz, Matthew, Teague, Samantha
- Authors: Shatte, Adrian , Hutchinson, Delyse , Fuller-Tyszkiewicz, Matthew , Teague, Samantha
- Date: 2020
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 23, no. 9 (2020), p. 611-618
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- Description: Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk"words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.
- Authors: Shatte, Adrian , Hutchinson, Delyse , Fuller-Tyszkiewicz, Matthew , Teague, Samantha
- Date: 2020
- Type: Text , Journal article
- Relation: Cyberpsychology, Behavior, and Social Networking Vol. 23, no. 9 (2020), p. 611-618
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- Description: Postpartum depression (PPD) is a significant mental health issue in mothers and fathers alike; yet at-risk fathers often come to the attention of health care professionals late due to low awareness of symptoms and reluctance to seek help. This study aimed to examine whether passive social media markers are effective for identifying fathers at risk of PPD. We collected 67,796 Reddit posts from 365 fathers, spanning a 6-month period around the birth of their child. A list of "at-risk"words was developed in collaboration with a perinatal mental health expert. PPD was assessed by evaluating the change in fathers' use of words indicating depressive symptomatology after childbirth. Predictive models were developed as a series of support vector machine classifiers using behavior, emotion, linguistic style, and discussion topics as features. The performance of these classifiers indicates that fathers at risk of PPD can be predicted from their prepartum data alone. Overall, the best performing model used discussion topic features only with a recall score of 0.82. These findings could assist in the development of support and intervention tools for fathers during the prepartum period, with specific applicability to personalized and preventative support tools for at-risk fathers. © Copyright 2020, Mary Ann Liebert, Inc., publishers 2020.
“You are not alone”: A big data and qualitative analysis of men's unintended fatherhood
- Smith, Imogene, Youssef, George, Shatte, Adrian, Teague, Samantha, Knight, Tess, Macdonald, Jacqui
- Authors: Smith, Imogene , Youssef, George , Shatte, Adrian , Teague, Samantha , Knight, Tess , Macdonald, Jacqui
- Date: 2022
- Type: Text , Journal article
- Relation: SSM. Qualitative research in health Vol. 2, no. (2022), p. 100085
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- Description: Becoming a father is a profound change in a man's life that is not always planned or wanted. Little is known about the subjective experiences of men who become fathers unintentionally or reluctantly. The aim of this research was to explore how men who did not intend to have children discuss their feelings about becoming a father in an online, anonymous environment. We sought insights into emotional responses, appraisals of family functioning, and relationships with infants. Data were collected from two Reddit forums for new and expectant fathers, r/Daddit and r/Predaddit. Approximately 2600 posts and 21,000 comments were extracted from the period between January 2019 and March 2020. We employed a two-stage methodology, blending big data techniques and qualitative analyses. Stage One included extraction and data preparation for topic modelling Stage Two was an adapted approach to thematic qualitative analysis. Topic modelling revealed 49 topics of which 6 were relevant thematically to unintended fatherhood. Men's communication in these were then classified within three domains: 1) Men's Concerns included their mental health, problems bonding with baby, their relationships with family and partner, and finances 2) Men's Affective Experiences existed on a spectrum of complex emotions including regret, resignation, ambivalence, acceptance, and excitement and 3) the Purpose of Communication included asking for and offering advice, normalisation, and perspective. Online forums like Reddit provide a unique opportunity for fathers who did not intend to have children to normalize their experience by expressing concerns and emotions in a pseudonymous environment. This study highlights the supportive environment that online discussions offer to fathers, and particularly unexpected fathers who may face stigma or barriers in other settings.
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