Multimedia social network is becoming a platform which helps user in many ways like connecting with each other , exchanges ideas and seek or provide help. To enhance people life most of the social network systems are canters around to extract knowledge from information. While Multimedia Social Networks (MSNs) apparently grow the user's ability to expand their social contacts, which may diminish their real contact from real outside world. In this way, the association of users and MSNs are

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... g progressively more complete. This proposed system expanded and enhanced the circumstance investigation system for the particular social area. It further proposed an algorithm to analyse user's intention which depends on exemplary Generalized Sequential Pattern (GSP). The proposed system utilized the enormous volume of user behaviour records for investigation of the continuous sequence mode which plays important role in the prediction of user intention. There are two important goals of experiments which are: playing and sharing of interactive media, which are most widely recognized in MSNs, based on the presented intention serialization algorithm under various least threshold limits. The proposed system found the ideal behaviour patterns for every user through Min Support by utilizing user's behaviour analysis on intentions. The behaviour patterns of each user are different on the basis of his/her different character which can be categorized by the content user shares or play in Social Media. The machine learning based, Social Network Mental Dis-order Detection (SNMDD) proposed additionally, which precisely recognize potential instances of SNMDDs to discover the stresses users of social networking sites through removed features from social network data.