[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117813-en":3,"doc-seo-117813-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117813,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",7,"Healthcare","An Investigation of Suicidal Ideation from Social Media Using Machine Learning Approach","Despite advances in identifying and treating severe mental disorders, suicide remains a major public health threat. This study evaluates machine-learning predictors of suicidal behavior by monitoring users’ social media posts to detect suicide ideation and related mental health risk. Analysis of social media signals is used to build a detection model and to clarify environmental risk factors shaping suicidal thoughts and conduct over time. Reported machine-learning results demonstrate strong performance with high accuracy, precision, recall, and F1-score, with SVM achieving the top accuracy (0.886).","An Investigation of Suicidal Ideation from Social Media Using Machine Learning Approach  \nSoumyabrataSaha , SuparnaDasgupta *, Adnan Anam , Rahul Saha , Sudarshan Nath  and Surajit Dutta   \n1Department of Information Technology, JIS College of Engineering, West Bengal, India.  \n*Corresponding Author.  \nReceived 03/02/2023, Revised 29/05/2023, Accepted 31/05/2023, Published 20/06/2023  \n This work is licensed under a Creative Commons Attribution 4.0 International License.  \nAbstract  \nDespite improvements in the detection and treatment of severe mental disorders, suicide remains a significant public health concern. Suicide prevention and control initiatives can benefit greatly from a thorough comprehension and foreseeability of suicide patterns. Understanding suicide patterns, especially through social media data analysis, can help in suicide prevention and control efforts. The objective of this study is to evaluate predictors of suicidal behavior in humans using machine learning. It is crucial to create a machine learning model for detection of suicide thoughts by monitoring a user's social media posts to identify warning signs of mental health issues. Through the analysis of social media posts, our research intends to develop a machine learning model for identifying suicide ideation and probable mental health problems. This study will help immensely to comprehend the environmental risk factors that influence suicidal thoughts and conduct across time. In this research the use of machine learning on social media data is an exciting new direction for understanding the environmental risk factors that impact an individual's susceptibility to suicide ideation and conduct over time. The machine learning algorithms showed high accuracy, precision, recall, and F1-score in detecting suicide patterns  \non social media data whereas SVM has the highest performance with an accuracy of 0.886. Keywords: Behavior, Ideation, Machine Learning, Prediction, Social Media, Suicide.  \nIntroduction  \nConforming to the World Health Organization, each year more than 800,000,000 individuals commit suicide 1-3. The WHO reports that suicide is the second greatest cause of mortality among 15–30-year-olds globally4-5. The suicide rate is high everywhere in the globe, not only in nations with the highest GDPs. About 77% of the world's suicides occurred in countries with low or moderate per capita income. Since its inception in 1948, suicide prevention has been one of the top priorities of WHO and they released its first global suicide prevention strategy in 1952. This plan puts an emphasis on training and development for professionals, creating  \nand implementing policies, mobilizing communities, educating and doing research. A wide range of emotions, but not limited to shock, wrath, guilt, sorrow, and worry, may contribute to suicidal thoughts, which is a severe issue affecting individuals of all ages. It can refer to the act of seriously considering, or even acting on, one's intent to commit suicide.  \nIn India more than 100000 individuals commit suicide each year. Professional career troubles, loneliness, abuse, violence, family issues, mental disorders, alcoholism, financial loss, chronic pain,  \nand many more circumstances might lead to suicide. The National Crime Records Bureau6 (NCRB) of India released data in 2022 showing that there were approx 1,64,033 suicides reported in India in 2021. This was a 7.2% increase from the number of suicides reported in 2020. The NCRB also reported that the suicide rate in India was 12 per lakh population in 2021, which is a 6.2% increase from the suicide rate in 2020.  \nStudies evaluating emergency room evaluations of suicidality have shown that adolescents are more likely to disclose suicidal ideation using electronic means, such as online forums, blogs, instant messengers, text messages, and emails. There is a correlation between online manifestations of suicidal ideation and psychometrically assessed suicide risk,","cbCaiuDLFXiOreCQ","https://ap.wps.com/l/cbCaiuDLFXiOreCQ","pdf",2122823,1,18,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the goal of this study on suicidal ideation?\",\"answer\":\"The study aims to evaluate predictors of suicidal behavior in humans using machine learning and to develop a detection model based on social media posts.\"},{\"question\":\"How does the proposed approach work using social media data?\",\"answer\":\"It monitors a user’s social media posts to identify warning signs and to classify suicide ideation and probable mental health problems from those signals.\"},{\"question\":\"Which machine learning algorithm showed the best performance?\",\"answer\":\"SVM showed the highest performance, achieving an accuracy of 0.886 in detecting suicide patterns on social media data.\"}]","An Investigation of Suicidal Ideation from Social Media Using Machine Learning Approach | PDF",1785679704,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"an-investigation-of-suicidal-ideation-from-social-media-using-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/an-investigation-of-suicidal-ideation-from-social-media-using-machine-learning-approach/117813/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of this study on suicidal ideation?","Question",{"text":75,"@type":76},"The study aims to evaluate predictors of suicidal behavior in humans using machine learning and to develop a detection model based on social media posts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach work using social media data?",{"text":80,"@type":76},"It monitors a user’s social media posts to identify warning signs and to classify suicide ideation and probable mental health problems from those signals.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning algorithm showed the best performance?",{"text":84,"@type":76},"SVM showed the highest performance, achieving an accuracy of 0.886 in detecting suicide patterns on social media data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]