scholarly journals Examining thematic similarity, difference, and membership in three online mental health communities from reddit: A text mining and visualization approach

2018 ◽  
Vol 78 ◽  
pp. 98-112 ◽  
Author(s):  
Albert Park ◽  
Mike Conway ◽  
Annie T. Chen
Keyword(s):  
Mental Health ◽  
Text Mining ◽  
Health Communities

scholarly journals Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1133
Author(s):  
Jingfang Liu ◽  
Jun Kong
Keyword(s):  
Mental Health ◽  
Text Mining ◽  
Mental Disorders ◽  
Empirical Analysis ◽  
Online Community ◽  
Information Quality ◽  
Social Experience ◽  
Adoption Model ◽  
Health Communities ◽  
Information Adoption

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

scholarly journals Best Paper Selection

2019 ◽  
Vol 28 (01) ◽  
pp. 179-180
Keyword(s):  
Breast Cancer ◽  
Mental Health ◽  
Text Mining ◽  
Drug Treatment ◽  
Topic Model ◽  
Web Based ◽  
Novel Approach ◽  
Health Communities ◽  
Model Approach

Abdellaoui R, Foulquié P, Texier N, Faviez C, Burgun A, Schück S. Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach. J Med Internet Res 2018;20(3):e85 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5874436/ Jones J, Pradhan M, Hosseini M, Kulanthaivel A, Hosseini M. Novel Approach to Cluster Patient-Generated Data Into Actionable Topics: Case Study of a Web-Based Breast Cancer. JMIR Med Inform 2018;6(4):e45 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6293240/ Park A, Conway M, Chen AT. Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach. Comput Human Behav 2018 Jan;78:98-112 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5810583/

scholarly journals Causal Factors of Effective Psychosocial Outcomes in Online Mental Health Communities

2020 ◽  
Author(s):  
Koustuv Saha ◽  
Amit Sharma
Keyword(s):  
Mental Health ◽  
Online Community ◽  
Psychosocial Outcomes ◽  
Case Control ◽  
Case Control Studies ◽  
Causal Factors ◽  
Health Distress ◽  
Health Communities ◽  
Mental Health Distress ◽  
And Control

Online mental health communities enable people to seek and provide support, and growing evidence shows the efficacy of community participation to cope with mental health distress. However, what factors of peer support lead to favorable psychosocial outcomes for individuals is less clear. Using a dataset of over 300K posts by ∼39K individuals on an online community TalkLife, we present a study to investigate the effect of several factors, such as adaptability, diversity, immediacy, and the nature of support. Unlike typical causal studies that focus on the effect of each treatment, we focus on the outcome and address the reverse causal question of identifying treatments that may have led to the outcome, drawing on case-control studies in epidemiology. Specifically, we define the outcome as an aggregate of affective, behavioral, and cognitive psychosocial change and identify Case (most improved) and Control (least improved) cohorts of individuals. Considering responses from peers as treatments, we evaluate the differences in the responses received by Case and Control, per matched clusters of similar individuals. We find that effective support includes complex language factors such as diversity, adaptability, and style, but simple indicators such as quantity and immediacy are not causally relevant. Our work bears methodological and design implications for online mental health platforms, and has the potential to guide suggestive interventions for peer supporters on these platforms.

scholarly journals Analysis of E-mental Health Research: Mapping the Relationship between Information Technology and Mental Healthcare

2021 ◽  
Author(s):  
tatsawan timakum ◽  
Min Song ◽  
Qing Xie
Keyword(s):  
Mental Health ◽  
Information Technology ◽  
Text Mining ◽  
Information Extraction ◽  
Health Research ◽  
Health Care Services ◽  
Text Message ◽  
Mental Healthcare ◽  
Mental Health Research ◽  
Mental Wellbeing

Abstract Background: E-mentalhealthcare is the convergence of digital technologies with mental health services. It has beendevelopedto fill a gap in healthcare for people who need mental wellbeing support and may never otherwise receive psychological treatment.This study aimed to apply text mining techniques to analyze the huge data of e-mental health researches and to report on research clusters and trends as well as the co-occurrence of biomedical and the use of information technology in this field.Methods: The e-mentalhealth research data was obtainedfrom 3,663 bibliographicrecords from Web of Science (WoS)and 3,172 full-text articlesfrom PubMed Central (PMC). The text mining techniques utilized for this study includedbibliometric analysis, information extraction, and visualization.Results: The e-mental health research topic trendsprimarily involvede-health care services and medical informatics research. The clusters of research comprise 16 clusters, which refer to mental sickness, ehealth, diseases, IT, and self-management. Based onthe information extraction analysis, in the biomedical domain, a “depression” entity was frequently detected and it pairs with other entities in the network with a betweenness centrality weighted at 0.046869 (eg. depression-online, depression-diabetes, depression-measure, and depression-mobile).The IT entity-relations of “mobile” were the most frequently found(weighted at 0.043466). The top pairs are related to depression, mobile health, and text message.Conclusions: E-mental health research trends focused on disease related-depression and using IT for treatment and prevention, primarily via online and mobile devices. Producing AI and machine learning are also being studied for e-mental healthcare. The results illustrate that physical sickness is likely to cause a mental health problem and identify the IT that was applied to help manage and mitigate mental health impacts.

scholarly journals Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study (Preprint)

2018 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Large Scale ◽  
New South ◽  
New South Wales ◽  
Mental Health Disorders ◽  
Free Text ◽  
South Wales ◽  
Health Disorders

BACKGROUND Vast numbers of domestic violence (DV) incidents are attended by the New South Wales Police Force each year in New South Wales and recorded as both structured quantitative data and unstructured free text in the WebCOPS (Web-based interface for the Computerised Operational Policing System) database regarding the details of the incident, the victim, and person of interest (POI). Although the structured data are used for reporting purposes, the free text remains untapped for DV reporting and surveillance purposes. OBJECTIVE In this paper, we explore whether text mining can automatically identify mental health disorders from this unstructured text. METHODS We used a training set of 200 DV recorded events to design a knowledge-driven approach based on lexical patterns in text suggesting mental health disorders for POIs and victims. RESULTS The precision returned from an evaluation set of 100 DV events was 97.5% and 87.1% for mental health disorders related to POIs and victims, respectively. After applying our approach to a large-scale corpus of almost a half million DV events, we identified 77,995 events (15.83%) that mentioned mental health disorders, with 76.96% (60,032/77,995) of those linked to POIs versus 16.47% (12,852/77,995) for the victims and 6.55% (5111/77,995) for both. Depression was the most common mental health disorder mentioned in both victims (22.30%, 3258) and POIs (18.73%, 8918), followed by alcohol abuse for POIs (12.24%, 5829) and various anxiety disorders (eg, panic disorder, generalized anxiety disorder) for victims (11.43%, 1671). CONCLUSIONS The results suggest that text mining can automatically extract targeted information from police-recorded DV events to support further public health research into the nexus between mental health disorders and DV.

scholarly journals Correction: Automatic Extraction of Mental Health Disorders From Domestic Violence Police Narratives: Text Mining Study

10.2196/13007 ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. e13007 ◽  
Author(s):  
George Karystianis ◽  
Armita Adily ◽  
Peter Schofield ◽  
Lee Knight ◽  
Clara Galdon ◽  
...  
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Text Mining ◽  
Mental Health Disorders ◽  
Automatic Extraction ◽  
Health Disorders

scholarly journals Effects of Community-Based Programs on Integration into the Mental Health and Non-Mental Health Communities

Healthcare ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1181
Author(s):  
Mi Kyung Seo ◽  
Min Hwa Lee
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Mental Health Service ◽  
Health Service ◽  
Community Integration ◽  
Equation Modeling ◽  
Community Based ◽  
Service Programs ◽  
Health Community ◽  
Health Communities

Aims: The purpose of this study was to verify how integration into the mental health community, a subculture of persons with mental illness, affects the integration into the non-mental health community. Thus, we analyzed the effect of community-based mental health service programs on non-mental health community integration, mediated by mental health community integration. Methods: In total, 190 persons with mental illness (M age = 42.78; SD = 11.3; male, 54.7%; female, 45.3%), living in local communities and using community-based mental health programs, participated in the study. We measured their sociodemographic and clinical variables, the environmental variables of mental health service programs, and the level of integration of the mental health and non-mental health communities. The data collected were analyzed to test the proposed hypotheses using Structural Equation Modeling (SEM). Results: The common significant predictors affecting the two types of community integration were symptoms and resource accessibility: the more accessible the various community resources and the less severe the psychiatric symptoms were, the higher the level of the two types of community integration was. In path analysis, the program’s atmosphere and the participation of people with mental illness (program involvement) significantly predicted the level of integration into the mental health community. This, in turn, had a positive effect on their physical integration, social contact frequency, and psychological integration into the non-mental health community, mediated by the integration of the mental health community. Conclusion: Based on the results, we emphasize the importance of mental health communities and suggest strategies to support the integration of mental health communities.

scholarly journals The Development of an Experience Measure for an Online Digital Mental Health Community

2021 ◽  
Author(s):  
Charlotte Mindel ◽  
Lily Mainstone-Cotton ◽  
Santiago de Ossorno Garcia ◽  
Aaron Sefi ◽  
Georgia Sugarman ◽  
...  
Keyword(s):  
Mental Health ◽  
Action Research ◽  
Participatory Action Research ◽  
Participatory Action ◽  
Digital Service ◽  
Modified Delphi ◽  
Health Community ◽  
Health Communities ◽  
Community Roles ◽  
Experience Measure

Online digital mental health communities can contribute to users’ mental health positively and negatively. Yet the measurement of outcomes and impact relating to digital mental health communities is difficult to capture. In this paper we demonstrate the development of an online experience measure for a specific children and young people’s community inside a digital mental health service. The development is informed by three phases: (i) item reduction through Estimate-Talk-Estimate modified Delphi methods, (ii) user testing with participatory action research and (iii) a pilot within the digital service community to explore its use. Rounds of experts talks help to reduce the items. User experience workshops helped to inform the usability and appearance, wording, and purpose of the measure. Finally, the pilot results highlight completion rates, difference in scores for age and community roles and a preference to ‘relate to others’; as a mechanism of support. Outcomes frequently selected in the measure show the importance of certain aspects of the community, such as safety, connection, and non-judgment previously highlighted in the literature. Self-reported helpfulness scales like this one could be used as indicators of meaningful engagement within the community and its content but further research is required to ascertain its acceptability and validity. Phased approaches involving stakeholders and participatory action research enhances the development of digitally enabled measurement tools.

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