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Research shows that many like-minded people use popular microbloggingwebsites for posting hateful speech against various religions and race.Automatic identification of racist and hate promoting posts is required forbuilding social media intelligence and security informatics based solutions.However, just keyword spotting based techniques cannot be used to accuratelyidentify the intent of a post. In this paper, we address the challenge of thepresence of ambiguity in such posts by identifying the intent of author. Weconduct our study on Tumblr microblogging website and develop a cascadedensemble learning classifier for identifying the posts having racist orradicalized intent. We train our model by identifying various semantic,sentiment and linguistic features from free-form text. Our experimental resultsshows that the proposed approach is effective and the emotion tone, socialtendencies, language cues and personality traits of a narrative arediscriminatory features for identifying the racist intent behind a post.

Characterizing Linguistic Attributes for Automatic Classification of Intent Based Racist/Radicalized Posts on Tumblr Micro-Blogging Website