Framework for detecting, assessing and mitigating mental health issue in the context of online social networks: a viewpoint paper

Purpose The development and presentation of a framework that integrates modern methods for detecting, assessing and mitigating mental health issues in the context of dynamic and adverse changes in social networks. Design/methodology/approach This viewpoint is based on a literature review of current advancements in the field. The use of causal discovery and causal inference methods forms the foundation for applying all the techniques included in the framework (machine learning, deep learning, explainable AI as well as large language models and generative AI). Additionally, an analysis of network effects and their influence on users’ emotional states is conducted. Findings The synergy of all methods used in the framework, combined with causal analysis, opens new horizons in predicting and diagnosing mental health disorders. The proposed framework demonstrates its applicability in providing additional analytics for the studied subjects (individual traits and factors that worsen mental health). It also proves its ability to identify hidden factors and processes. Originality/value The proposed framework offers a novel perspective on addressing mental health issues in the context of rapidly evolving digital platforms. Its flexibility allows for the adaptation of tools and methods to various scenarios and user groups. Its application can contribute to the development of more accurate algorithms that account for the impact of negative (including hidden) external factors affecting users. Furthermore, it can assist in the diagnostic process.