[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118066-en":3,"doc-seo-118066-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118066,1099523882367,"Hazel","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques","Social media provides young users a platform to express difficulties and opinions in a digital environment, enabling analysis of behavioral signals at scale. Mental disorders remain common yet frequently overlooked, and online content can support early identification of mental health conditions. This study predicts mental health disorders among Twitter users using machine learning models, comparing Support Vector Machine, Decision Tree, and Naive Bayes. Model performance and accuracy are evaluated to determine the most effective approach for disorder detection from tweet text.","Predicting Mental Health Disorder On Twitter Using  \nMachine Learning Techniques  \nShi Ru Lim Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang, Malaysia [shirulim6148@gmail.com](shirulim6148@gmail.com)  \nNur Shazwani Kamarudin* Faculty of Computing Universiti Malaysia Pahang AlSultan Abdullah Pahang, Malaysia[nshazwani@ump.edu.my](nshazwani@ump.edu.my)  \nNur Hafieza Ismail Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang, Malaysia [hafieza@ump.edu.my](hafieza@ump.edu.my)  \n2023 IEEE 8th International Conference On Software Engineering and Computer Systems (ICSECS) ©2023 IEEE DOI: 10.1109/ICSECS58457.2023.10256420| 979-8-3503-1093-1/23/$31.00 |   \nNik Ahmad Hisham Ismail  \nKulliyyah of Education International Islamic University Malaysia Selangor, Malaysia  \n[nikahmad@iium.edu.my](nikahmad@iium.edu.my)  \nAbstract—Social media gives young people a place to voice their difficulties and trade opinions on current events in the digital era. Therefore, it is possible to analyze human behavior using internet media. However, the illness of mental disorder is common yet often ignored. Social media makes it possible to identify mental health disorders in large populations. Many efforts have been made to evaluate individual postings using machine learning techniques to identify people with mental health conditions on social media. This study attempted to predict mental health disorders among Twitter users using machine learning techniques. Support Vector Machine (SVM), Decision Tree, and Naive Bayes are three examples of machine learning approaches applied in this study. To assess the algorithms, the performance and accuracy of these three algorithms are compared.  \nKeywords—Twitter, mental health, machine learning, prediction, accuracy  \nI. INTRODUCTION  \nSocial networks have altered how people communicate their thoughts and points of view. This change is made available through written publications, internet discussion boards, product review websites, etc. This user-generated content is significantly relied upon by people. Social networks provide a sizable volume of user-generated content, which is crucial for research. They also offer additional services tailored to users’demands [1] . The most widely used source of information for user opinions and feelings expressed on this platform is Twitter, which can be retrieved and examined. Social networking sites are altering people’s lives and why they communicate or connect with the rest of the world. According to recent studies, many people use social networking sites like Facebook and Twitter for various activities, including finding and sharing information, making new friends, joining existing ones, and simply having [2] .  \nMore research is being done on social media and mental health, connecting social media use and behavior with stress, anxiety, depression, suicidality, and other mental diseases [3] . The majority of this study is focused on mental illness. Being balanced inside oneself explains why mental health is a critical and fundamental component of total health. Additionally, the ability to form and maintain emotional attachments with other people, engage in social activities and cultural obligations, and recognize and accept emotions and sentiments like happiness or sadness are all  \nNor Ashikin Mohamad Kamal Faculty of Computer and Mathematical Science Universiti Teknologi Mara  \nSelangor, Malaysia  \n[ashikin@uitm.edu.my](ashikin@uitm.edu.my)  \nindications of a person’s mental health. It is referred to as mental illness when this essential functioning is absent. Numerous social, biological, and psychological factors can impact mental illness, just like they do mental wellness. According to experts, internal issues ranging from a lack of emotional resilience to low social standing and solitude make mental health susceptible [15] . Mental health may be defined more simply as a person’s thinking about themselves and their lives. It","cbCaifYKGlpnqDTp","https://ap.wps.com/l/cbCaifYKGlpnqDTp","pdf",880169,1,"English","en",105,"# Abstract\n# Introduction\n## Social media and mental health background\n## Sentiment analysis and NLP/ML approach\n# Problem Statement","[{\"question\":\"What is the main goal of the study on Twitter data?\",\"answer\":\"The study aims to predict mental health disorders among Twitter users by analyzing tweet content using machine learning techniques.\"},{\"question\":\"Which machine learning algorithms are compared in this work?\",\"answer\":\"Support Vector Machine (SVM), Decision Tree, and Naive Bayes are compared as three applied approaches for disorder prediction.\"},{\"question\":\"How does the study plan to detect disorders from tweets?\",\"answer\":\"The approach uses sentiment analysis with text mining and natural language processing to extract sentiment-related signals from Twitter text, supported by keyword-based crawling of relevant tweets.\"}]","Predicting Mental Health Disorder On Twitter Using Machine Learning Techniques | PDF",1785681217,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"predicting-mental-health-disorder-on-twitter-using-machine-learning-techniques","",{"@graph":35,"@context":84},[36,52,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/predicting-mental-health-disorder-on-twitter-using-machine-learning-techniques/118066/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-09-04","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main goal of the study on Twitter data?","Question",{"text":74,"@type":75},"The study aims to predict mental health disorders among Twitter users by analyzing tweet content using machine learning techniques.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which machine learning algorithms are compared in this work?",{"text":79,"@type":75},"Support Vector Machine (SVM), Decision Tree, and Naive Bayes are compared as three applied approaches for disorder prediction.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the study plan to detect disorders from tweets?",{"text":83,"@type":75},"The approach uses sentiment analysis with text mining and natural language processing to extract sentiment-related signals from Twitter text, supported by keyword-based crawling of relevant tweets.","https://schema.org",{"og:url":50,"og:type":86,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":88,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":105,"slug":137},19,"General","general"]