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Twitter is prone to malicious tweets containing URLs for spar, phishing, and malware distribution. Conventional Twitter spar detection schemes utilize account features such as the ratio of tweets containing URLs and the account creation date, or relation features in the Twitter graph. These detection schemes are ineffective against feature fabrications or consume much time and resources. Conventional suspicious URL detection schemes utilize several features including lexical features of URLs, URL redirection, HTIUIL content, and dynamic behavior. However, evading techniques such as time-based evasion and crawler evasion exist. in this paper, we propose WARNINGBIRD, a suspicious URL detection system for Twitter. Our system investigates correlations of URL redirect chains extracted from several tweets. Because attackers have limited resources and usually reuse them, their URL redirect chains frequently …