[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126212-en":3,"doc-seo-126212-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126212,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","A Systematic Review on Leveraging Machine Learning and Deep Learning for Early Mental Health Depression Detection on Social Media Platforms","Machine learning and deep learning approaches for detecting depression from social media content offer a scalable path to address limitations of traditional diagnosis methods. This systematic literature review summarizes recent work that uses user-generated posts to identify depressive symptoms early, emphasizing the benefits of unstructured text modeling and the potential of large language models and neural networks for high-accuracy detection. It also discusses interpretability and transparency challenges and the need to integrate model outputs with clinical data and broader mental health monitoring frameworks. Findings support researchers and healthcare professionals building innovative early detection and management systems.","Alakananda K, Ananth Prabhu G, Melwin D Souza, Vidya VV, A Systematic Review on Leveraging Machine Learning and Deep Learning for Early Mental health Depression detection on Social Media Platforms.  \nSEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nA Systematic Review on Leveraging Machine Learning and Deep Learning for Early Mental health Depression detection on Social Media Platforms  \n1Alakananda K, 2Ananth Prabhu G, 3Melwin D Souza, 4Vidya VV  \n1,2,4Department of Computer Science and Engineering, Sahyadri College of Engineering and Management, Mangalore, India  \n3Department of Computer Science and Engineering, Moodlakatte Institute of Technology Kundapura, India  \nKEYWORDS  \nRoBERTa,DistilBT, Electra, GPT-3.5 Turbo 1106 model (proprietary model), LLaMA2- 7B(opensource), BXCNN Model, BERT, XGBoost, and CNN  \nABSTRACT:  \nThe use of machine learning and deep learning models in detecting depression on social media has become a promising approach to address the challenges in traditional depression diagnosis methods. This systematic literature review examines the recent advancements in the application of these models to analyze social media data for early identification of depressive symptoms. The review highlights the advantages of leveraging user-generated content on social media platforms, as well as the potential of large language models and neural networks in achieving high accuracy in depression detection. However, the review also discusses the need to address challenges related to the interpretability and transparency of these models, and the importance of integrating them with clinical data and comprehensive mental health monitoring frameworks. The findings ofthis review provide valuable insights for researchers and healthcare professionals interested in utilizing innovative technologies to enhance the early detection and management of depression. Social media platforms have emerged as a valuable source of data for analyzing various mental health conditions, including depression. Researchers have explored the potential of leveraging textual content from social media posts to detect and monitor depressive disorders, as individuals often express their mental health struggles and experiences on these platforms. This paper presents a comprehensive literature review on the current state of research in machine learning models for mental health analysis on social media.  \n1. Introduction  \nLakhs of people are suffering from mental illness due to unavailability of early treatment and services for depression detection. Thus, it is a very challenging task to recognize people who are suffering from mental health disorders and provide them treatments as early as possible. It is the major reason for anxiety disorder, bipolar disorder, sleeping disorder, depression and sometimes it may lead to selfharm and suicide.  \nThe widespread use of social media platforms has provided researchers with a wealth of user-generated data that can be leveraged for early detection of mental health conditions, such as depression (Garg, 2023) [9]. The use of machine learning and deep learning models has emerged as a promising approach to analyze social media data and identify patterns indicative of depressive symptoms.  \nThese advanced computational techniques offer several advantages over traditional depression diagnosis methods (Zhang et al., 2024) [10](Shah et al., 2024) [11], including the ability to process large volumes of unstructured textual data, detect subtle linguistic and behavioral cues, and achieve high accuracy in predicting the onset of depressive episodes.  \nAlakananda K, Ananth Prabhu G, Melwin D Souza, Vidya VV, A Systematic Review on Leveraging Machine Learning and Deep Learning for Early Mental health Depression detection on Social Media Platforms.  \nSEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nThis systematic literature review examines the recent advancements in the application of machine lear","cbCaibRckWXK7YBL","https://ap.wps.com/l/cbCaibRckWXK7YBL","pdf",836146,9,1,23,"English","en",105,"# Introduction\n# Methods\n## Data sources and social media platforms\n## Mental health taxonomy and automation context","[{\"question\":\"Why is early depression detection important in mental health care?\",\"answer\":\"Early identification helps provide timely intervention and can reduce downstream risks associated with depression, including suicide-related outcomes. It addresses the difficulty created by limited availability of early treatment and specialized diagnostic resources.\"},{\"question\":\"How do machine learning and deep learning models contribute to depression detection on social media?\",\"answer\":\"They analyze user-generated textual data to detect linguistic and behavioral cues associated with depressive symptoms. This enables processing large volumes of unstructured information and improving prediction accuracy for early onset of depressive episodes.\"},{\"question\":\"What challenges does the review highlight beyond detection accuracy?\",\"answer\":\"The review emphasizes the need to address interpretability and transparency of these models. It also stresses integrating computational outputs with clinical data and comprehensive mental health monitoring frameworks.\"}]","A Systematic Review on Leveraging Machine Learning and Deep Learning for Early Mental Health Depression Detection on Social Media Platforms | PDF",1785903814,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"a-systematic-review-on-leveraging-machine-learning-and-deep-learning-for-early-mental-health-depression-detection-on-social-media-platforms","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/a-systematic-review-on-leveraging-machine-learning-and-deep-learning-for-early-mental-health-depression-detection-on-social-media-platforms/126212/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"Why is early depression detection important in mental health care?","Question",{"text":77,"@type":78},"Early identification helps provide timely intervention and can reduce downstream risks associated with depression, including suicide-related outcomes. It addresses the difficulty created by limited availability of early treatment and specialized diagnostic resources.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How do machine learning and deep learning models contribute to depression detection on social media?",{"text":82,"@type":78},"They analyze user-generated textual data to detect linguistic and behavioral cues associated with depressive symptoms. This enables processing large volumes of unstructured information and improving prediction accuracy for early onset of depressive episodes.",{"name":84,"@type":75,"acceptedAnswer":85},"What challenges does the review highlight beyond detection accuracy?",{"text":86,"@type":78},"The review emphasizes the need to address interpretability and transparency of these models. It also stresses integrating computational outputs with clinical data and comprehensive mental health monitoring frameworks.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,122,125,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":120,"slug":121},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":123,"slug":124},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]