• Miss.Punam Ashok Bane
• Prof.S.S.Nagtilak

The enormous growth and volume of online social networks and their features, along with the vast number of socially connected users, it has become difficult to explain the true semantic value of published content for the detection of user behaviors. Without understanding the contextual background, it is impractical to differentiate among various groups in terms of their relevance and mutual relations, or to identify the most significant representatives from the community at large. In this paper, we propose an integrated social media network content analysis platform that leverages three levels of features, i.e., user-generated content, social sensing network, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users.

Malicious activity, social network, user behaviors.

"User Behavior to Identify Malicious Activities in Large-Scale Social Networks.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.173-179, May-2019, Available :http://www.jetir.org/papers/JETIR1905H29.pdf

"User Behavior to Identify Malicious Activities in Large-Scale Social Networks.", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp173-179, May-2019, Available at : http://www.jetir.org/papers/JETIR1905H29.pdf