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153030 https://hdl.handle.net/2286/R.I.26799 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2014 vii, 51 p. : col. ill Masters Thesis Academic theses Text eng Rajadesingan, Ashwin Liu, Huan Kambhampati, Subbarao Pon-Barry, Heather Arizona State University Partial requirement for: M.S., Arizona State University, 2014 Includes bibliographical references (p. 46-49) Field of study: Computer science Sarcasm is a nuanced form of language where usually, the speaker explicitly states the opposite of what is implied. Imbued with intentional ambiguity and subtlety, detecting sarcasm is a difficult task, even for humans. Current works approach this challenging problem primarily from a linguistic perspective, focusing on the lexical and syntactic aspects of sarcasm. In this thesis, I explore the possibility of using behavior traits intrinsic to users of sarcasm to detect sarcastic tweets. First, I theorize the core forms of sarcasm using findings from the psychological and behavioral sciences, and some observations on Twitter users. Then, I develop computational features to model the manifestations of these forms of sarcasm using the user's profile information and tweets. Finally, I combine these features to train a supervised learning model to detect sarcastic tweets. I perform experiments to extensively evaluate the proposed behavior modeling approach and compare with the state-of-the-art. Computer Science behavior modeling Data Mining sarcasm detection Social Computing social media mining Online social networks Data Mining Human behavior models Irony--Social aspects. Irony Sarcasm detection on Twitter: a behavioral modeling approach