AlSumait Loulwah, AlFarhan Altaf, AlHeneidi Hasah
Department of Information Science, Kuwait University, Kuwait City, Kuwait.
Liberal Arts Department, American University of the Middle East, Egaila, Kuwait.
PeerJ Comput Sci. 2025 Apr 25;11:e2828. doi: 10.7717/peerj-cs.2828. eCollection 2025.
This article defines an artificial intelligence framework to detect individual's mental health (MH) status on social networks. The proposed framework, which consists of four main modules, aims to analyze the emotions that are expressed by social network users in their text posts and identify their mental coping strategies, resources, and demands based on The Demands-Resources-Individual Effects (DRIVE) model. Although sentiment analysis (SA) is effective in analyzing the polarity of the text, it is limited in detecting the mental health status in terms of the coping strategies, available resources, or encountered stressors. This study illustrates such limitations in detecting the coping strategies and shows the effectiveness of the coping-based analysis. The work also reveals the phrases and topics that were used by individuals to express their coping strategies which provides a novel outlook of the individuals' psychological coping within their environment.
The social network X is used to collect the coping strategies expressed by people who experienced stress during COVID-19 from November 2019 to May 2022. Text was processed using natural language processing (NLP). A sample of posts was coded into a positive or negative coping category and one of eight subtypes. SA and statistical analysis were performed to compare SA results with coded coping strategies. Latent Dirichlet Allocation and bigram NLP were applied to identify main themes and terminologies. Coping classification models were created and tested.
The findings reveal that 70% of posts show positive coping strategies. The main positive coping themes included self-care, seeking help, positive reframing, engaging in prayers and meditation, employing humor through sarcasm, and implementing a practical mindset. Conversely, the remaining 30% of posts expressed negative coping themes, such as conspiracy thoughts, wishful or hopeless thinking, and negative perceptions. The coping classification models achieved a reliable predictive level with an average accuracy of 74.8%. Categorizing coping strategies using SA methods, particularly TextBlob and VADER, revealed high miscategorization rates, especially for negative coping strategies. Bigrams and LDA analysis identified distinct word patterns in positive and negative coping strategies, with emojis playing a significant role in emotional expression across both categories.
The article defined a framework for a MH detector based on the DRIVE model. It highlighted the resilience and adaptive responses of individuals in times of crisis. It also focused on coping and identified physical, emotional, and social support and positive reframing as major positive strategies; and the spread of false information and loss of social support as negative coping strategies. The applied coping classification models showed reliable performance in distinguishing between positive and negative coping categories.
本文定义了一个人工智能框架，用于在社交网络上检测个人的心理健康（MH）状况。所提出的框架由四个主要模块组成，旨在分析社交网络用户在文本帖子中表达的情绪，并根据需求 - 资源 - 个体效应（DRIVE）模型识别他们的心理应对策略、资源和需求。尽管情感分析（SA）在分析文本的极性方面很有效，但在检测心理健康状况方面，在应对策略、可用资源或遇到的压力源方面存在局限性。本研究说明了在检测应对策略方面的此类局限性，并展示了基于应对的分析的有效性。这项工作还揭示了个人用来表达其应对策略的短语和主题，这为个人在其环境中的心理应对提供了新的视角。
使用社交网络X收集2019年11月至2022年5月期间在新冠疫情期间经历压力的人们所表达的应对策略。使用自然语言处理（NLP）对文本进行处理。将一部分帖子编码为积极或消极应对类别以及八种亚型之一。进行情感分析和统计分析，以将情感分析结果与编码的应对策略进行比较。应用潜在狄利克雷分配和二元语法自然语言处理来识别主要主题和术语。创建并测试应对分类模型。
研究结果表明，70%的帖子显示出积极的应对策略。主要的积极应对主题包括自我照顾、寻求帮助、积极重新构建、进行祈祷和冥想、通过讽刺运用幽默以及采取务实的心态。相反，其余30%的帖子表达了消极应对主题，如阴谋论想法、一厢情愿或绝望的想法以及消极看法。应对分类模型达到了可靠的预测水平，平均准确率为74.8%。使用情感分析方法（特别是TextBlob和VADER）对应对策略进行分类，显示出较高的错误分类率，尤其是对于消极应对策略。二元语法和潜在狄利克雷分配分析在积极和消极应对策略中识别出不同的词模式，表情符号在两类情感表达中都发挥了重要作用。
本文定义了一个基于DRIVE模型的心理健康检测器框架。它强调了个人在危机时期的恢复力和适应性反应。它还关注应对，并确定身体、情感和社会支持以及积极重新构建为主要的积极策略；而虚假信息的传播和社会支持的丧失为消极应对策略。所应用的应对分类模型在区分积极和消极应对类别方面表现出可靠的性能。
