Decoding Depression: An In-Depth Review of Multimedia-Based Detection on Social Media

Depression is one of the most common mental illness conditions, affecting millions of people in today's world, which can be generally described by persistent sadness, hopelessness, and loss of interest in daily activities. With the rapid adoption of social media, more and more people now express their feelings through various forms of multimedia, text, images, and audio, thereby creating a vast digital footprint that can be very useful for monitoring mental health with automated systems. This paper analyses the use of multimedia content on social networks in the detection of depressive behaviors. It discusses AI and deep learning techniques that include sentiment analysis, NLP, image recognition, and voice emotion recognition which might be useful to identify depressive signs from such multimedia inputs. The review has covered several issues on challenges that arise in multimedia-based detection, such as data privacy, algorithmic bias, and the ethical repercussions of mental health diagnosis by a system of automation. Its mention of concerns surrounding user confidentiality, reducing bias in AI models, and making proper balances between human oversight and automation is very important. Finally, the paper presents possible future work that could ensure the accuracy, timeliness, and ethical standards of depression detection systems toward more responsible technology-driven mental health care.