As per health care perception Depression is a wellbeing concern at global range. In today social media’s enhancement allows the people those who affected can share their experiences through posts. Such experiences are stored in database and can be extract and analyze to assist the precautions for others or recall the drugs from side effects, and other service improvements in their particular disease treatment. In those aspects social websites related to depression are helpful to extract knowledge or monitor on various types of drugs and its side effects and also for sharing their experiences on depression. We have taken a weighted edge network model for representing social networks activities. The proposed work undergoes with the three steps. The first step is for user activity monitoring and followed by network clustering and module analysis. Whoever the person like a specific posts belongs to a group and those who are not are belong to other group. We implemented the stop word technique here which is helpful in avoiding the misleading communication on the posts and efficient interaction of user. The statistical analyses of such user interactions are beneficial for health networks to acquire more knowledge on particular disease. This approach enables us all the gatherings took a part and for healthcare improvements in future to the patients of that disease.