Detection of Mental Health Issues and Solution to Online Toxicity using Machine Learning

In modern times, the use of the internet and social media has greatly increased, leading to the emergence of online toxicity, which can have serious consequences for individuals' mental health. Machine learning techniques can be used to detect mental health issues and address online toxicity. One approach to detecting mental health issues in its early stages is to use machine learning to analyze social media posts and other online content. By training a model on large datasets of labeled text, it is possible to identify patterns and indicators of mental health issues such as depression, anxiety, and stress. To address online toxicity, machine learning can be used to identify and classify toxic content, such as hate speech or cyberbullying. This can be done by training a model on a dataset of labeled toxic and non-toxic content Once the model is trained, it can be used to identify and flag the toxic content in real-time, allowing it to be moderated or removed completely. Overall, the use of machine learning for the detection of mental health issues and the solution to online toxicity can be a powerful tool for promoting mental wellbeing and creating a safer and more positive online environment.