Predicting Readers' Sarcasm Understandability by Modeling Gaze Behavior


  • Abhijit Mishra Indian Institute of Technology Bombay
  • Diptesh Kanojia Indian Institute of Technology Bombay
  • Pushpak Bhattacharyya Indian Institute of Technology Bombay



sarcasm understandability, eye movement, sarcasm understandability prediction, eye tracking, eye movement pattern, context incongruity, multi-instance learning


Sarcasm understandability or the ability to understand textual sarcasm depends upon readers' language proficiency, social knowledge, mental state and attentiveness. We introduce a novel method to predict the sarcasm understandability of a reader. Presence of incongruity in textual sarcasm often elicits distinctive eye-movement behavior by human readers. By recording and analyzing the eye-gaze data, we show that eye-movement patterns vary when sarcasm is understood vis-à-vis when it is not. Motivated by our observations, we propose a system for sarcasm understandability prediction using supervised machine learning. Our system relies on readers' eye-movement parameters and a few textual features, thence, is able to predict sarcasm understandability with an F-score of 93%, which demonstrates its efficacy. The availability of inexpensive embedded-eye-trackers on mobile devices creates avenues for applying such research which benefits web-content creators, review writers and social media analysts alike.




How to Cite

Mishra, A., Kanojia, D., & Bhattacharyya, P. (2016). Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behavior. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).