A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE
Seasonal Affective Disorder (SAD) is a mood disorder characterized by recurring depressive episodes during specific seasons, particularly in Fall and Winter. With the rise of social media as a platform for self-expression, user-generated content offers valuable insights into mental health trends, pr...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
|
| Series: | Acta Psychologica |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S000169182500318X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849727855780429824 |
|---|---|
| author | Md. Shamshuzzoha Tazkia Tasnim Bahar Audry Md. Jahangir Alam Zaheed Ahmed Bhuiyan Md Motaharul Islam Mohammad Mehedi Hassan |
| author_facet | Md. Shamshuzzoha Tazkia Tasnim Bahar Audry Md. Jahangir Alam Zaheed Ahmed Bhuiyan Md Motaharul Islam Mohammad Mehedi Hassan |
| author_sort | Md. Shamshuzzoha |
| collection | DOAJ |
| description | Seasonal Affective Disorder (SAD) is a mood disorder characterized by recurring depressive episodes during specific seasons, particularly in Fall and Winter. With the rise of social media as a platform for self-expression, user-generated content offers valuable insights into mental health trends, presenting an opportunity for data-driven SAD detection. However, existing research faces challenges such as limited multimodal datasets, class imbalance, and the need for real-time predictive models. This study addresses these gaps by curating a unique social media dataset that captures seasonal patterns and employing advanced machine learning techniques for accurate SAD detection. We apply the Synthetic Minority Over-sampling Technique (SMOTE) in two distinct ways—on the training dataset post-splitting and the entire dataset—to assess its impact on model generalization. Our findings highlight Random Forest, LGBM, and XGBoost as the top-performing models, with K-Nearest Neighbors (KNN) achieving the highest accuracy of 97.87 % in the training dataset. Additionally, we optimize computational efficiency to ensure real-time scalability for large-scale social media data processing. This research advances SAD detection by integrating robust dataset curation, class imbalance mitigation, and machine learning optimization, paving the way for more effective mental health monitoring through social media analytics. |
| format | Article |
| id | doaj-art-d080fea6ee4e4744b2030f0c8df3ef7d |
| institution | DOAJ |
| issn | 0001-6918 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Acta Psychologica |
| spelling | doaj-art-d080fea6ee4e4744b2030f0c8df3ef7d2025-08-20T03:09:44ZengElsevierActa Psychologica0001-69182025-06-0125610500510.1016/j.actpsy.2025.105005A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTEMd. Shamshuzzoha0Tazkia Tasnim Bahar Audry1Md. Jahangir Alam2Zaheed Ahmed Bhuiyan3Md Motaharul Islam4Mohammad Mehedi Hassan5Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, BangladeshDepartment of Electrical and Computer Engineering, Morgan State University, 1700, E Cold Spring Ln, Baltimore, MD 21251, United StatesDepartment of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, BangladeshDepartment of Computer Science and Engineering, United International University, United City, Madani Avenue, Badda, Dhaka 1212, Bangladesh; Corresponding author.Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaSeasonal Affective Disorder (SAD) is a mood disorder characterized by recurring depressive episodes during specific seasons, particularly in Fall and Winter. With the rise of social media as a platform for self-expression, user-generated content offers valuable insights into mental health trends, presenting an opportunity for data-driven SAD detection. However, existing research faces challenges such as limited multimodal datasets, class imbalance, and the need for real-time predictive models. This study addresses these gaps by curating a unique social media dataset that captures seasonal patterns and employing advanced machine learning techniques for accurate SAD detection. We apply the Synthetic Minority Over-sampling Technique (SMOTE) in two distinct ways—on the training dataset post-splitting and the entire dataset—to assess its impact on model generalization. Our findings highlight Random Forest, LGBM, and XGBoost as the top-performing models, with K-Nearest Neighbors (KNN) achieving the highest accuracy of 97.87 % in the training dataset. Additionally, we optimize computational efficiency to ensure real-time scalability for large-scale social media data processing. This research advances SAD detection by integrating robust dataset curation, class imbalance mitigation, and machine learning optimization, paving the way for more effective mental health monitoring through social media analytics.http://www.sciencedirect.com/science/article/pii/S000169182500318XSADSocial media analyticsSentiment analysisSAD detectionMLSMOTE |
| spellingShingle | Md. Shamshuzzoha Tazkia Tasnim Bahar Audry Md. Jahangir Alam Zaheed Ahmed Bhuiyan Md Motaharul Islam Mohammad Mehedi Hassan A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE Acta Psychologica SAD Social media analytics Sentiment analysis SAD detection ML SMOTE |
| title | A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE |
| title_full | A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE |
| title_fullStr | A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE |
| title_full_unstemmed | A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE |
| title_short | A novel framework for seasonal affective disorder detection: Comprehensive machine learning analysis using multimodal social media data and SMOTE |
| title_sort | novel framework for seasonal affective disorder detection comprehensive machine learning analysis using multimodal social media data and smote |
| topic | SAD Social media analytics Sentiment analysis SAD detection ML SMOTE |
| url | http://www.sciencedirect.com/science/article/pii/S000169182500318X |
| work_keys_str_mv | AT mdshamshuzzoha anovelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT tazkiatasnimbaharaudry anovelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT mdjahangiralam anovelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT zaheedahmedbhuiyan anovelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT mdmotaharulislam anovelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT mohammadmehedihassan anovelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT mdshamshuzzoha novelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT tazkiatasnimbaharaudry novelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT mdjahangiralam novelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT zaheedahmedbhuiyan novelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT mdmotaharulislam novelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote AT mohammadmehedihassan novelframeworkforseasonalaffectivedisorderdetectioncomprehensivemachinelearninganalysisusingmultimodalsocialmediadataandsmote |