Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities
BackgroundEarly maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems and are central in schema therapy. However, the characteristics of EMSs vary across demographics, and despite the growing use of o...
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JMIR Publications
2025-02-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e59524 |
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| author | Beng Heng Ang Sujatha Das Gollapalli Mingzhe Du See-Kiong Ng |
| author_facet | Beng Heng Ang Sujatha Das Gollapalli Mingzhe Du See-Kiong Ng |
| author_sort | Beng Heng Ang |
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BackgroundEarly maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems and are central in schema therapy. However, the characteristics of EMSs vary across demographics, and despite the growing use of online mental health communities (OMHCs), how EMSs manifest in these online support-seeking environments remains unclear. Understanding these characteristics could inform the design of more effective interventions powered by artificial intelligence to address online support seekers’ unique therapeutic needs.
ObjectiveWe aimed to uncover associations between EMSs and mental health problems within OMHCs and examine features of EMSs as they are reflected in OMHCs.
MethodsWe curated a dataset of 29,329 posts from widely accessed OMHCs, labeling each with relevant schemas and mental health problems. To identify associations, we conducted chi-square tests of independence and calculated odds ratios (ORs) with the dataset. In addition, we developed a novel group-level case conceptualization technique, leveraging GPT-4 to extract features of EMSs from OMHC texts across key schema therapy dimensions, such as schema triggers and coping responses.
ResultsSeveral associations were identified between EMSs and mental health problems, reflecting how EMSs manifest in online support-seeking contexts. Anxiety-related problems typically highlighted vulnerability to harm or illness (OR 5.64, 95% CI 5.34-5.96; P<.001), while depression-related problems emphasized unmet interpersonal needs, such as social isolation (OR 3.18, 95% CI 3.02-3.34; P<.001). Conversely, problems with eating disorders mostly exemplified negative self-perception and emotional inhibition (OR 1.89, 95% CI 1.45-2.46; P<.001). Personality disorders reflected themes of subjugation (OR 2.51, 95% CI 1.86-3.39; P<.001), while posttraumatic stress disorder problems involved distressing experiences and mistrust (OR 5.04, 95% CI 4.49-5.66; P<.001). Substance use disorder problems reflected negative self-perception of failure to achieve (OR 1.83, 95% CI 1.35-2.49; P<.001). Depression, personality disorders, and posttraumatic stress disorder were also associated with 12, 9, and 7 EMSs, respectively, emphasizing their complexities and the need for more comprehensive interventions. In contrast, anxiety, eating disorder, and substance use disorder were related to only 2 to 3 EMSs, suggesting that these problems are better addressed through targeted interventions. In addition, the EMS features extracted from our dataset averaged 13.27 (SD 3.05) negative features per schema, with 2.65 (SD 1.07) features per dimension, as supported by existing literature.
ConclusionsWe uncovered various associations between EMSs and mental health problems among online support seekers, highlighting the prominence of specific EMSs in each problem and the unique complexities of each problem in terms of EMSs. We also identified EMS features as expressed by support seekers in OMHCs, reinforcing the relevance of EMSs in these online support-seeking contexts. These insights are valuable for understanding how EMS are characterized in OMHCs and can inform the development of more effective artificial intelligence–powered tools to enhance support on these platforms. |
| format | Article |
| id | doaj-art-7a49167501ba432a9f76a2ab47bacac8 |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | JMIR Publications |
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| series | Journal of Medical Internet Research |
| spelling | doaj-art-7a49167501ba432a9f76a2ab47bacac82025-02-07T19:30:52ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-02-0127e5952410.2196/59524Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health CommunitiesBeng Heng Anghttps://orcid.org/0009-0008-3096-195XSujatha Das Gollapallihttps://orcid.org/0000-0002-4567-8937Mingzhe Duhttps://orcid.org/0000-0001-7832-0459See-Kiong Nghttps://orcid.org/0000-0001-6565-7511 BackgroundEarly maladaptive schemas (EMSs) are pervasive, self-defeating patterns of thoughts and emotions underlying most mental health problems and are central in schema therapy. However, the characteristics of EMSs vary across demographics, and despite the growing use of online mental health communities (OMHCs), how EMSs manifest in these online support-seeking environments remains unclear. Understanding these characteristics could inform the design of more effective interventions powered by artificial intelligence to address online support seekers’ unique therapeutic needs. ObjectiveWe aimed to uncover associations between EMSs and mental health problems within OMHCs and examine features of EMSs as they are reflected in OMHCs. MethodsWe curated a dataset of 29,329 posts from widely accessed OMHCs, labeling each with relevant schemas and mental health problems. To identify associations, we conducted chi-square tests of independence and calculated odds ratios (ORs) with the dataset. In addition, we developed a novel group-level case conceptualization technique, leveraging GPT-4 to extract features of EMSs from OMHC texts across key schema therapy dimensions, such as schema triggers and coping responses. ResultsSeveral associations were identified between EMSs and mental health problems, reflecting how EMSs manifest in online support-seeking contexts. Anxiety-related problems typically highlighted vulnerability to harm or illness (OR 5.64, 95% CI 5.34-5.96; P<.001), while depression-related problems emphasized unmet interpersonal needs, such as social isolation (OR 3.18, 95% CI 3.02-3.34; P<.001). Conversely, problems with eating disorders mostly exemplified negative self-perception and emotional inhibition (OR 1.89, 95% CI 1.45-2.46; P<.001). Personality disorders reflected themes of subjugation (OR 2.51, 95% CI 1.86-3.39; P<.001), while posttraumatic stress disorder problems involved distressing experiences and mistrust (OR 5.04, 95% CI 4.49-5.66; P<.001). Substance use disorder problems reflected negative self-perception of failure to achieve (OR 1.83, 95% CI 1.35-2.49; P<.001). Depression, personality disorders, and posttraumatic stress disorder were also associated with 12, 9, and 7 EMSs, respectively, emphasizing their complexities and the need for more comprehensive interventions. In contrast, anxiety, eating disorder, and substance use disorder were related to only 2 to 3 EMSs, suggesting that these problems are better addressed through targeted interventions. In addition, the EMS features extracted from our dataset averaged 13.27 (SD 3.05) negative features per schema, with 2.65 (SD 1.07) features per dimension, as supported by existing literature. ConclusionsWe uncovered various associations between EMSs and mental health problems among online support seekers, highlighting the prominence of specific EMSs in each problem and the unique complexities of each problem in terms of EMSs. We also identified EMS features as expressed by support seekers in OMHCs, reinforcing the relevance of EMSs in these online support-seeking contexts. These insights are valuable for understanding how EMS are characterized in OMHCs and can inform the development of more effective artificial intelligence–powered tools to enhance support on these platforms.https://www.jmir.org/2025/1/e59524 |
| spellingShingle | Beng Heng Ang Sujatha Das Gollapalli Mingzhe Du See-Kiong Ng Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities Journal of Medical Internet Research |
| title | Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities |
| title_full | Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities |
| title_fullStr | Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities |
| title_full_unstemmed | Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities |
| title_short | Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities |
| title_sort | unraveling online mental health through the lens of early maladaptive schemas ai enabled content analysis of online mental health communities |
| url | https://www.jmir.org/2025/1/e59524 |
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