Digital technologies and online platforms have radically reshaped how individuals express, share, and experience mental health and well-being. Social media platforms, wearable devices, and other digital traces offer an unprecedented opportunity to understand, monitor, and even intervene in mental health challenges at scale. This special issue invites interdisciplinary research that uses Artificial Intelligence (AI), Natural Language Processing (NLP), Social Media Analytics, and Multimodal Data Mining to address pressing questions in mental health research, diagnosis, and care.

This issue aims to bridge computational innovation with clinical relevance, emphasizing studies that:

• Extract meaningful mental health insights from digital data,

• Develop tools for early detection and intervention,

• Tackle algorithmic bias and ethical issues in mental health prediction,

• Foster well-being and resilience through digital technologies.

Topics of Interest (include but are not limited to):

• Mental health detection and prediction using NLP and machine learning on social media platforms and integration into clinical decision-making

• AI-powered chatbots and conversational agents for mental health support and digital therapy in clinical or counseling contexts

• Clinical case studies of AI deployment for mental health screening, diagnosis, or therapy augmentation

• Explainable and interpretable AI for mental health decision-making in clinical or digital settings

• Collaborative approaches between AI developers and healthcare providers for co-design of digital mental health tools

• Hate speech, Hope speech and emotional resilience: Combating hate speech and associative mental impact, Identifying and promoting positive discourse and support in online communities

• Multimodal analysis for mental health, including text, images, videos, and speech

• AI-powered chatbots and conversational agents for mental health support and digital therapy

• Digital phenotyping and behavioral modeling using smartphone, wearable, and app usage data

• Bias, fairness, and ethical implications of AI models used in mental health screening or diagnostics

• Detection of harmful or triggering content, misinformation, and mental health-related toxicity online

• Explainable and interpretable AI for mental health decision-making in clinical or digital settings

• Low-resource and cross-cultural approaches to digital mental health analytics, especially in the Global South

• Digital interventions and campaigns that promote mental well-being through social platforms or mobile apps

This Collection also strongly encourages contributions from clinicians, psychiatrists, psychologists, and public health professionals who utilize digital platforms in diagnostics, therapy delivery, or patient monitoring. We aim to bridge computational approaches with clinical expertise to ensure the development of AI tools that are not only technically robust but also practically deployable in real-world mental healthcare settings.

Keywords:Mental Health Informatics, Clinical Natural Language Processing (CNLP), Digital Mental Health, AI for Mental Health Prediction, Multimodal Behavioral Analysis, Digital Interventions and Therapy, Clinical Mental Health Tools, Digital Psychiatry, AI in Psychological Assessment, Behavioral Health Informatics, Patient-Centered AI