Abstract：
BACKGROUND: Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.
OBJECTIVE: This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS: A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS: Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS: Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.
OBJECTIVE: This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.
METHODS: A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.
RESULTS: Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.
CONCLUSIONS: Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.
摘要：
背景：基于文本的数字媒体平台彻底改变了通信和信息共享，在心理健康和自杀预防领域提供宝贵的知识和理解。
目的：本系统综述旨在确定如何将机器学习和数据分析应用于基于文本的数字媒体数据，以了解心理健康并帮助预防自杀。
方法：对来自以下主要电子数据库的研究论文进行了系统综述：WebofScience，MEDLINE,Embase（通过MEDLINE），和PsycINFO（通过MEDLINE）。使用GoogleScholar进行手动搜索，以补充数据库搜索。
结果：总体而言，包括19项研究，关于如何应用数据分析和机器学习技术的五个主要主题：(1)作为个人心理健康的预测指标，（2）了解个人心理健康和自杀行为是如何沟通的，（3）检测精神障碍和自杀风险，(4)确定寻求帮助的心理健康困难，(5)确定支持心理健康的干预措施的有效性。
结论：我们的研究结果表明，数据分析和机器学习可用于获得有价值的见解，例如：与抑郁症有关的基于网络的对话在不同种族之间有所不同，青少年比成年人更频繁地进行关于自杀的网络对话，在基于网络的心理健康社区寻求支持的人在获得在线支持后感觉更好。数字工具和心理健康应用程序正在成功用于管理心理健康，特别是通过COVID-19的流行，在此期间，分析显示焦虑和抑郁增加，基于网络的社区在减少大流行期间的孤立方面发挥了作用。预测分析也被证明具有潜力，虚拟现实在提供预防性或治疗性护理方面显示出有希望的结果。未来的研究工作可以集中在优化算法上，以增强基于文本的数字媒体分析在心理健康和自杀预防方面的潜力。在解决抑郁症时，关键的一步是确定导致幸福的因素，并使用机器学习来预测这些幸福的来源。这可以扩展到理解各种活动如何在不同的社会经济群体中提高幸福感。利用从这些数据分析和机器学习中收集的见解，有机会制定数字干预措施，比如聊天机器人，旨在提供支持和解决心理健康挑战和自杀预防。
目的：本系统综述旨在确定如何将机器学习和数据分析应用于基于文本的数字媒体数据，以了解心理健康并帮助预防自杀。
方法：对来自以下主要电子数据库的研究论文进行了系统综述：WebofScience，MEDLINE,Embase（通过MEDLINE），和PsycINFO（通过MEDLINE）。使用GoogleScholar进行手动搜索，以补充数据库搜索。
结果：总体而言，包括19项研究，关于如何应用数据分析和机器学习技术的五个主要主题：(1)作为个人心理健康的预测指标，（2）了解个人心理健康和自杀行为是如何沟通的，（3）检测精神障碍和自杀风险，(4)确定寻求帮助的心理健康困难，(5)确定支持心理健康的干预措施的有效性。
结论：我们的研究结果表明，数据分析和机器学习可用于获得有价值的见解，例如：与抑郁症有关的基于网络的对话在不同种族之间有所不同，青少年比成年人更频繁地进行关于自杀的网络对话，在基于网络的心理健康社区寻求支持的人在获得在线支持后感觉更好。数字工具和心理健康应用程序正在成功用于管理心理健康，特别是通过COVID-19的流行，在此期间，分析显示焦虑和抑郁增加，基于网络的社区在减少大流行期间的孤立方面发挥了作用。预测分析也被证明具有潜力，虚拟现实在提供预防性或治疗性护理方面显示出有希望的结果。未来的研究工作可以集中在优化算法上，以增强基于文本的数字媒体分析在心理健康和自杀预防方面的潜力。在解决抑郁症时，关键的一步是确定导致幸福的因素，并使用机器学习来预测这些幸福的来源。这可以扩展到理解各种活动如何在不同的社会经济群体中提高幸福感。利用从这些数据分析和机器学习中收集的见解，有机会制定数字干预措施，比如聊天机器人，旨在提供支持和解决心理健康挑战和自杀预防。
