Applying Machine Learning to Detect and Measure Depression via Social Media

This study introduces an innovative machine learning approach to detect and measure depression on social media platforms. By harnessing the potential of advanced neural network architectures and analyzing social media data, our methodology exhibits promising outcomes in accurately identifying depression indicators. This approach holds promise for facilitating early detection, intervention, and support for individuals experiencing depression. However, ethical considerations and privacy concerns must be carefully addressed when working with social media data. The authors used a variety of machine learning techniques and performed in-depth comparison analyses to identify depression. The authors evaluated the accuracy scores and found that the multimodal ensemble had the best accuracy of 86%. It is essential to emphasize that our models are intended as an aid for mental health professionals rather than a substitute for professional diagnosis and treatment. Overall, this research contributes to the field of mental health research by providing insights into individuals’ mental well-being through the analysis of social media data.