机器学习在社交媒体用户自杀意念检测中的应用综述

doi:10.3778/j.issn.1673-9418.2405086

摘要/Abstract

摘要： 近年来，社交媒体平台成为人类发布情感甚至是自杀意念、企图和行为的崭新阵地，并且成为自杀意念检测的重要数据平台和关键评估依据。随着人工智能技术的兴起，关于机器学习在社交媒体用户自杀意念检测中的应用研究成为热点。但在国内，该领域相关研究较为匮乏，尚未形成完整体系。为系统梳理其研究现状及发展脉络，对机器学习技术赋能自杀意念检测的研究进行了全面总结。概述了自杀意念检测的定义、流程、常见方法及评价指标，总结了目前自杀意念检测任务中常用的数据集和现有特征工程及其技术。分别从传统的机器学习和深度学习的角度对自杀意念检测进行了系统总结，对比分析了每种方法的关键技术、核心思想及优缺点。归纳了当前该领域中亟待解决的问题及创新解决方法，特别介绍了ChatGPT等大语言模型、多模态模型在该领域的应用。讨论了机器学习在社交媒体自杀意念检测应用研究中的局限性，并提出了未来的研究方向，以期进一步推动形成数据驱动、人机协同、跨学科融合、跨文化畛域的数智化自杀意念检测新范式。

关键词: 自杀意念检测, 社交媒体, 机器学习, 深度学习, 特征提取

Abstract: In recent years, social media platforms have emerged as a novel domain for individuals to express their emotions, including suicidal ideation, attempts, and behaviors. Consequently, these platforms have evolved into crucial data reposi- tories and essential assessment criteria for detecting suicidal ideation. With the advent of artificial intelligence technology, the utilization of machine learning in detecting suicidal ideation among social media users has emerged as a scintillating subject. However, in China, the relative research is scarce and has not yet established a comprehensive system. To systematically review the research status and development context of suicide ideation detection, this paper presents a comprehensive summary of machine learning technology in empowering suicide ideation detection. Firstly, this paper provides an overview of the definition, process, commonly employed methods, and evaluation indicators for detecting suicidal ideation. Secondly, this paper provides a comprehensive overview of suicide ideation detection techniques, encompassing both traditional machine learning and deep learning approaches. The key methodologies, fundamental concepts, merits, and limitations of each method are thoroughly compared and analyzed. Furthermore, the urgent issues and innovative solutions in this field are summarized, with a particular focus on the application of large language models such as ChatGPT and multi-modal models. Finally, the limitations of machine learning in the application research of suicidal ideation detection on social media are comprehensively discussed, and future research directions are proposed, in order to further promote the formation of a new paradigm of data-driven, human-computer collaboration, interdisciplinary integration, and cross-cultural domain of suicide ideation detection.

Key words: suicidal ideation detection, social media, machine learning, deep learning, feature extraction

蒙秀扬, 王世屹, 李渡渡, 王春玲. 机器学习在社交媒体用户自杀意念检测中的应用综述[J]. 计算机科学与探索, 2025, 19(3): 559-581.

MENG Xiuyang, WANG Shiyi, LI Dudu, WANG Chunling. Review on Application of Machine Learning in Detecting Suicidal Ideation for Social Media Users[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(3): 559-581.

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