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The impact of continually evolving digital technologies and the proliferationof communications and content has now been widely acknowledged to be central tounderstanding our world. What is less acknowledged is that this is based on thesuccessful arousing of curiosity both at the collective and individual levels.Advertisers, communication professionals and news editors are in constantcompetition to capture attention of the digital population perennially shiftyand distracted. This paper, tries to understand how curiosity works in thedigital world by attempting the first ever work done on quantifying humancuriosity, basing itself on various theories drawn from humanities and socialsciences. Curious communication pushes people to spot, read and click themessage from their social feed or any other form of online presentation. Ourapproach focuses on measuring the strength of the stimulus to generate readercuriosity by using unsupervised and supervised machine learning algorithms, butis also informed by philosophical, psychological, neural and cognitive studieson this topic. Manually annotated news headlines - clickbaits - have beenselected for the study, which are known to have drawn huge reader response. Abinary classifier was developed based on human curiosity (unlike the work doneso far using words and other linguistic features). Our classifier shows anaccuracy of 97% . This work is part of the research in computational humanitieson digital politics quantifying the emotions of curiosity and outrage ondigital media.

How Curiosity can be modeled for a Clickbait Detector