Clustering Help-Seeking Behaviors in LGBT Online Communities: A Prospective Trial

Liang, Chen; Abbott, Douglas A.; Hong, Yan; Madadi, Mahboubeh; White, Amelia

doi:10.1007/978-3-030-21902-4_25

Cited by 7 publications

(26 citation statements)

References 16 publications

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“…Of these, we excluded articles that did not meet the target population criteria of the LGBTQ2S+ population (3/21, 14%), full-texts could not be retrieved (1/21, 4%), unrelated to ML (4/21, 19%), duplicate article published in a conference proceeding (1/21, 4%), and a commentary (1/21, 4%). This resulted in 11 studies being included in the final review [ 36 - 46 ]. The detailed selection process of the articles is presented in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram ( Figure 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…All 11 included studies [ 36 - 46 ] were published within the last 2 years ( Table 1 ). Most of the studies were carried out in the United States (7/11, 63%) [ 36 , 38 , 39 , 41 - 43 , 45 ].…”

Section: Resultsmentioning

confidence: 99%

“…All 11 included studies [ 36 - 46 ] were published within the last 2 years ( Table 1 ). Most of the studies were carried out in the United States (7/11, 63%) [ 36 , 38 , 39 , 41 - 43 , 45 ]. Among the target population categories that were not mutually exclusive, sexual minority men (gay, men who have sex with men, bisexual) were the most commonly studied (5/11, 45%) subgroups [ 37 , 40 , 42 - 44 ], followed by transgender (3/11, 27%) [ 39 , 45 , 46 ] and LGBTQ+ (3/11, 27%) [ 36 , 38 , 41 ] people at large, whereas sexual minority women (lesbian and bisexual) (2/11, 18%) [ 43 , 45 ] were the least commonly represented populations.…”

Section: Resultsmentioning

confidence: 99%

“…Most of the studies focused on mental health (9/11, 82%) [ 36 - 42 , 45 , 46 ], and only 18% (2/11) studies [ 43 , 44 ] focused on substance use concerns. Most studies examined several mental health issues, such as depression, suicide, mood or affect processes, minority stress, and gender incongruence [ 36 - 42 , 45 , 46 ], whereas other studies that focused on substance use only examined tobacco and poppers or alkyl nitrites use [ 43 , 44 ]. No study looked into mental health issues and substance use concerns among the LGBTQ2S+ population simultaneously ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…The studies were categorized into 3 major ML domains: web content analysis, prediction modeling, and imaging study. Over half of the studies (6/11, 55%) were identified as web content analysis [ 36 - 41 ], and 36% (4/11) were identified as prediction modeling [ 42 - 45 ]; 1 study (9%) was identified as an imaging study [ 46 ] ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

See 4 more Smart Citations

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Kundu¹,

Chaiton²,

Billington³

et al. 2021

JMIR Med Inform

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Background A high risk of mental health or substance addiction issues among sexual and gender minority populations may have more nuanced characteristics that may not be easily discovered by traditional statistical methods. Objective This review aims to identify literature studies that used machine learning (ML) to investigate mental health or substance use concerns among the lesbian, gay, bisexual, transgender, queer or questioning, and two-spirit (LGBTQ2S+) population and direct future research in this field. Methods The MEDLINE, Embase, PubMed, CINAHL Plus, PsycINFO, IEEE Xplore, and Summon databases were searched from November to December 2020. We included original studies that used ML to explore mental health or substance use among the LGBTQ2S+ population and excluded studies of genomics and pharmacokinetics. Two independent reviewers reviewed all papers and extracted data on general study findings, model development, and discussion of the study findings. Results We included 11 studies in this review, of which 81% (9/11) were on mental health and 18% (2/11) were on substance use concerns. All studies were published within the last 2 years, and most were conducted in the United States. Among mutually nonexclusive population categories, sexual minority men were the most commonly studied subgroup (5/11, 45%), whereas sexual minority women were studied the least (2/11, 18%). Studies were categorized into 3 major domains: web content analysis (6/11, 54%), prediction modeling (4/11, 36%), and imaging studies (1/11, 9%). Conclusions ML is a promising tool for capturing and analyzing hidden data on mental health and substance use concerns among the LGBTQ2S+ population. In addition to conducting more research on sexual minority women, different mental health and substance use problems, as well as outcomes and future research should explore newer environments, data sources, and intersections with various social determinants of health.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Kundu¹,

Chaiton²,

Billington³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

Interaction with peers online: LGBTQIA+ individuals' information seeking and meaning‐making during the life transitions of identity construction

Jia,

Du,

Zhao

2023

Asso for Info Science & Tech

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People search for information and experiences and seek meaning as a common reaction to new life challenges. There is little knowledge about the interactions through which experiential information is acquired, and how such interactions are meaningful to an information seeker. Through a qualitative content analysis of 992 posts in an online forum, this study investigated lesbian, gay, bisexual, transgender, intersex, queer/questioning, asexual (LGBTQIA+) individuals' online information interactions and meaning‐making with peers during their life transitions of identity construction. Our analysis reveals LGBTQIA+ people's life challenges across three transition stages (being aware of, exploring, and living with a new identity). Three main types of online peer interactions were identified within: cognitive, affective, and situational peer interactions. We found that online peer interactions are not only a type of information source that LGBTQIA+ individuals use to acquire understanding about themselves but a unique space for transformation learning and meaning‐making where they share self‐examination and reflection, conduct assessments and assumptions, and obtain strength and skills to initiate and adapt life transitions. The findings have theoretical contributions to the development of information behavior models of transitions and practical implications on providing information services that support LGBTQIA+ individuals' meaning‐making during the life transition.

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“Being Able to Be Yourself” : A Qualitative Exploration of How Queer Emerging Adults Experience a Sense of Belonging in Rural Australia

Marlin

Lewis

McLaren

2022

Journal of Homosexuality

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Clustering Help-Seeking Behaviors in LGBT Online Communities: A Prospective Trial

Cited by 7 publications

References 16 publications

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Machine Learning Applications in Mental Health and Substance Use Research Among the LGBTQ2S+ Population: Scoping Review

Interaction with peers online: LGBTQIA+ individuals' information seeking and meaning‐making during the life transitions of identity construction

“Being Able to Be Yourself” : A Qualitative Exploration of How Queer Emerging Adults Experience a Sense of Belonging in Rural Australia

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