Cross platform social media analysis for mental health detection

While mental health challenges have increasingly become a major concern globally, social media platforms provide tremendous data for, among others, analyzing emotional and behavioral patterns. This survey synthesizes methodologies from a comprehensive set of various research papers focusing on the analysis of mental health using cross-platform data and gives a critical review of their implications, strengths, and limitations. Advanced transformer-based models like BERT and RoBERTa have emerged as the best among leading tools of textual analysis, showing unprecedented precision, contextual understanding, and interpretability in the detection of depression, PTSD (post-traumatic stress disorder), and anxiety. Apart from text-based insights, there is a great need to investigate, through behavioral studies, the social networks, community interactions, and engagement dynamics that provide valuable perspectives on what may be underlying factors for mental health. The multimodal approaches, therefore, using a combination of text, audio, and visual data introduce a more complete diagnostic framework with higher accuracy. However, most of the time, these are plagued by computational complexity, resource intensity, and challenges in handling diverse data sources. Besides these, there are also some critical ethical challenges to overcome with respect to privacy, consent, and algorithmic bias for effective deployment in the real world. This survey points to certain key gaps in terms of diversity of datasets, scalability of solutions, and absence of robust ethical protocols, while giving directions for future work in integrating state-of-the-art machine learning methodologies with interdisciplinary ethical frameworks. These findings further strengthen the transformative potential of advanced AI-driven approaches in revolutionizing mental health care and advancing our understanding of emotional and behavioral health at both individual and societal levels.