背景:在2023年,美国经历了最高记录的自杀人数,超过5万人死亡。在精神疾病领域,抑郁症是最常见的问题,影响15%至17%的人口,并携带约15%的显著自杀风险。然而,不是每个抑郁症患者都有自杀念头.虽然“自杀性抑郁症”不是临床诊断,它可以在日常生活中观察到,强调意识的必要性。
目的:本研究旨在研究动力学,情感音调,以及r/Depressionsubreddit中帖子中讨论的主题,特别关注也参与过r/SuicideWatch社区的用户。目的是使用自然语言处理技术和模型来更好地了解具有潜在自杀意念的用户抑郁的复杂性,目的是改善自杀的干预和预防策略。
方法:存档的帖子是从2019年到2022年的r/Depression和r/SuicideWatchReddit社区中提取的,最终数据集超过150,000个帖子,由大约25,000个独特的重叠用户提供。对这些职位进行了广泛和全面的混合方法,包括趋势和生存分析,探索2个子reddits中用户的动态。BERT系列模型从数据中提取特征用于情感和主题分析。
结果:2020年8月16日,r/SuicideWatch的帖子计数超过了r/Depression。2020年从r/Depression到r/SuicideWatch的过渡时间最短,只持续了26天。悲伤是r/Depression社区中重叠用户中最普遍的情绪。此外,身体活动的变化,消极的自我观点,自杀念头被认为是最常见的抑郁症状,都与失望的情绪基调表现出较强的正相关。此外,除了自杀念头外,“在学校和工作中与抑郁和动机作斗争”(12%)成为讨论最多的话题,根据用户对自杀意念的倾向对用户进行分类。
结论:我们的研究强调了在r/Depression和r/SuicideWatch等在线社区中使用自然语言处理技术探索与心理健康挑战相关的语言标记和模式的有效性。这些见解提供了不同于以往研究的新颖观点。在未来,使用这些技术将有可能进一步完善和优化机器分类,这可能导致更有效的干预和预防策略。
BACKGROUND: In 2023, the United States experienced its highest- recorded number of suicides, exceeding 50,000 deaths. In the realm of psychiatric disorders, major depressive disorder stands out as the most common issue, affecting 15% to 17% of the population and carrying a notable suicide risk of approximately 15%. However, not everyone with depression has suicidal thoughts. While \"suicidal depression\" is not a clinical diagnosis, it may be observed in daily life, emphasizing the need for awareness.
OBJECTIVE: This study aims to examine the dynamics, emotional tones, and topics discussed in posts within the r/Depression subreddit, with a specific focus on users who had also engaged in the r/SuicideWatch community. The objective was to use natural language processing techniques and models to better understand the complexities of depression among users with potential suicide ideation, with the goal of improving intervention and prevention strategies for suicide.
METHODS: Archived posts were extracted from the r/Depression and r/SuicideWatch Reddit communities in English spanning from 2019 to 2022, resulting in a final data set of over 150,000 posts contributed by approximately 25,000 unique overlapping users. A broad and comprehensive mix of methods was conducted on these posts, including trend and survival analysis, to explore the dynamic of users in the 2 subreddits. The BERT family of models extracted features from data for sentiment and thematic analysis.
RESULTS: On August 16, 2020, the post count in r/SuicideWatch surpassed that of r/Depression. The transition from r/Depression to r/SuicideWatch in 2020 was the shortest, lasting only 26 days. Sadness emerged as the most prevalent emotion among overlapping users in the r/Depression community. In addition, physical activity changes, negative self-view, and suicidal thoughts were identified as the most common depression symptoms, all showing strong positive correlations with the emotion tone of disappointment. Furthermore, the topic \"struggles with depression and motivation in school and work\" (12%) emerged as the most discussed topic aside from suicidal thoughts, categorizing users based on their inclination toward suicide ideation.
CONCLUSIONS: Our study underscores the effectiveness of using natural language processing techniques to explore language markers and patterns associated with mental health challenges in online communities like r/Depression and r/SuicideWatch. These insights offer novel perspectives distinct from previous research. In the future, there will be potential for further refinement and optimization of machine classifications using these techniques, which could lead to more effective intervention and prevention strategies.