AI framework for DRIVE model based mental health detection in text: a case study on how coping strategies are expressed during COVID-19

Background 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. Methods 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. Results 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. Conclusion 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.