Title: Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation Show Chinese title

Title: 理解社交媒体上的心理健康内容及其对自杀意念的影响

Abstract: This review underscores the critical need for effective strategies to identify and support individuals with suicidal ideation, exploiting technological innovations in ML and DL to further suicide prevention efforts. The study details the application of these technologies in analyzing vast amounts of unstructured social media data to detect linguistic patterns, keywords, phrases, tones, and contextual cues associated with suicidal thoughts. It explores various ML and DL models like SVMs, CNNs, LSTM, neural networks, and their effectiveness in interpreting complex data patterns and emotional nuances within text data. The review discusses the potential of these technologies to serve as a life-saving tool by identifying at-risk individuals through their digital traces. Furthermore, it evaluates the real-world effectiveness, limitations, and ethical considerations of employing these technologies for suicide prevention, stressing the importance of responsible development and usage. The study aims to fill critical knowledge gaps by analyzing recent studies, methodologies, tools, and techniques in this field. It highlights the importance of synthesizing current literature to inform practical tools and suicide prevention efforts, guiding innovation in reliable, ethical systems for early intervention. This research synthesis evaluates the intersection of technology and mental health, advocating for the ethical and responsible application of ML, DL, and NLP to offer life-saving potential worldwide while addressing challenges like generalizability, biases, privacy, and the need for further research to ensure these technologies do not exacerbate existing inequities and harms. Show Chinese abstract

Abstract: 这篇综述强调了制定有效策略以识别和支持有自杀意念个体的紧迫性，利用机器学习和深度学习的技术创新来进一步推进自杀预防工作。 该研究详细描述了这些技术在分析大量非结构化社交媒体数据中的应用，以检测与自杀想法相关的语言模式、关键词、短语、语气和上下文线索。 它探讨了各种机器学习和深度学习模型，如支持向量机、卷积神经网络、长短期记忆网络、神经网络及其在解释文本数据中的复杂数据模式和情感细微差别的有效性。 该综述讨论了这些技术作为救命工具的潜力，通过个体的数字痕迹来识别高风险人群。 此外，它评估了在自杀预防中使用这些技术的实际效果、局限性和伦理考量，并强调了负责任开发和使用的重要性。 该研究旨在通过分析该领域的最新研究、方法、工具和技术来填补关键的知识空白。 它强调了综合现有文献以指导实用工具和自杀预防工作的必要性，引导可靠、符合伦理的系统在早期干预中的创新。 这项研究综合评估了技术和心理健康之间的交叉点，倡导对机器学习、深度学习和自然语言处理进行道德和负责任的应用，以在全球范围内提供挽救生命的潜力，同时解决诸如泛化性、偏见、隐私问题以及进一步研究的必要性，以确保这些技术不会加剧现有的不平等和伤害。