Detection of Stress Based on Social Media Blogs using ML (original) (raw)

People are increasingly using online platforms to share their feelings, opinions, and experiences as a result of the exponential rise of social media platforms. This abundance of user-generated material is an important resource for researching mental health and human behaviour. This abstract gives a summary of a study that aims to identify stress through social media blogs. This study's goal is to create an algorithm for assessing stress levels in people via the examination of their internet-based blog postings. The results of this study will have a number of repercussions. First, by providing an automated method for stress identification and monitoring, it will advance the study of mental health. This could help mental health experts spot people who might need help or intervention. The study will also clarify the function of internet use in mental health and contribute to greater awareness of the potential negative effects of online expressiveness on stress levels. Additionally, the study will investigate the connections between psychological stress and other elements including demographic data and historical trends. The model can provide light on the contextual elements that affect stress levels in various people by looking at these linkages.