Predicting readers' sarcasm understandability by modeling gaze behavior

Abstract

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 eyemovement patterns vary when sarcasm is understood vis-a-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' eyemovement 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.