Today online social networks play an important role in daily life. There are various online social network(Twitter) are there and these shows tremendous growth in recent years. These kind of social networks allow users to make social connection with others. Apart from all these there are some security issues or security violations are there. This paperrelated to the system investigates correlations of URL redirect chains extracted from several tweets in Twitter. Because attackers have limited resources and usually reuse them, their URL redirect chains frequently share the same URLs. To develop methods to discover correlated URL redirect chains using the frequently shared URLs and to determine their suspiciousness. So collect numerous tweets from the Twitter public timeline and build a statistical classifier using them. Evaluation results show that our classifier accurately and efficiently detects suspicious URLs.