[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123752-en":3,"doc-seo-123752-105":30,"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":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},123752,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",7,"Healthcare","Suicide and self-harm prediction based on social media data using machine learning algorithms - Abstract","Online social networking generates context- and time-rich data streams that can support predicting suicidal ideation and related behaviour. Despite the promise of digital media, modelling of acute suicidal ideation remains limited. This study applies machine learning models to a previously published Instagram dataset of youths, using language-use and activity-based predictors. It evaluates out-of-sample, cross-validated performance, compares results with earlier work, and uses model explanations to assess predictor relevance and subject-level patterns.","Suicide and self-harm prediction based on social media data using machine learning algorithms  \nAbdulrazak Yahya Saleh a, 1,*, Fadzlyn Nasrini Binti Mostapa a,2  \na Faculty of Cognitive Sciences and Human Development, Universiti Malaysia Sarawak, Malaysia [1](1 abdulrazakalhababi@gmail.com)[ abdulrazakalhababi@gmail.com](1 abdulrazakalhababi@gmail.com); [2](2 69662@siswa.unimas.my)[ 69662@siswa.unimas.my](2 69662@siswa.unimas.my)  \n* Corresponding Author  \nARTICLE  \nINFO  \nABSTRACT  \n\n| Article history\u003Cbr>Received April, 04, 2023 Revised April, 11, 2023 Accepted May, 06, 2023\u003Cbr>Keywords\u003Cbr>Social networking Machine learning Algorithms Suicide\u003Cbr>Self-harm | Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital\u003Cbr>This is an open access article under the CC–BY-SA license.\u003Cbr> |\n| --- | --- |\n\nbiomarkers and assess their external validity with more rigor.  \n1. Introduction  \nThe term \"social media\" refers to an online platform that enables social interaction, such as Facebook, Twitter, YouTube, and Instagram [1] . [2] assert that social media is fundamentally defined by three critical concepts which are cognition, communication, and cooperation. These concepts endow social media with a variety of various forms of sociality, including information, facts, and knowledge, activities, relationships, communities, and partnerships [2] . The numerous forms of sociality that social media supports allowing the platform to serve a variety of critical roles for its users, notably communication, relationship creation and maintenance, and thus a platform for providing knowledge [3] . These features are highly valued by social media users, particularly young adults. Youth are classified as ardent social media users in a variety of research conducted globally [3] .  \nMalaysian youth are likewise reported to be avid social media users. While there is no debate about the use of social media [4], it is also necessary to exercise caution regarding the medium's hazards. Spam hoaxes, cyberbullying, online harassment, and sexting are just a few of the dangers [1] . Youth who use social media risk privacy assaults and depression [1]. These dangers can be avoided if youth possess an  \nacceptable level of social media competency. Presently, social media platforms such as Facebook and Instagram have become the primary sources of information for assisting people of modern society in adjusting to their new lifestyle. Indeed, the global population of social media users is predicted to reach 3.02 billion by 2021, and one of the important uses of social media may be to encourage healthy lifestylesand to enhance people's management of their health status [5] . In Malaysia specifically, a 2018 poll performed by the Malaysian Communications and Multimedia Commission (MCMC) discovered that many Malaysian internet users, particularly young users, shared content online via social media (61.8 percent) . According to the survey, 97.3 percent of Malaysians use Fa","cbCaivAothJDdc0r","https://ap.wps.com/l/cbCaivAothJDdc0r","pdf",346175,1,2,"English","en",105,"# Abstract\n# Introduction\n## Background and risks of social media\n## Relevance to youth and Malaysia\n# Method\n## Data Preparation\n## Datasets","[{\"question\":\"What data source is used for suicide and self-harm prediction in this study?\",\"answer\":\"The study uses a previously published Instagram dataset of youths containing suicide-indicative and non-suicidal posts.\"},{\"question\":\"How does the study prepare data before building prediction models?\",\"answer\":\"Data preparation assembles suicide and self-harm datasets on social media, split into data collection and data processing phases.\"},{\"question\":\"Which predictors are used for the machine learning models?\",\"answer\":\"Predictors reflect language use and activity patterns within social networking, aiming to inform the likelihood that a post shows suicidal and self-harm intent.\"}]","Suicide and self-harm prediction based on social media data using machine learning algorithms - 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