  The impact of continually evolving digital technologies and the proliferation
of communications and content has now been widely acknowledged to be central to
understanding our world. What is less acknowledged is that this is based on the
successful arousing of curiosity both at the collective and individual levels.
Advertisers, communication professionals and news editors are in constant
competition to capture attention of the digital population perennially shifty
and distracted. This paper, tries to understand how curiosity works in the
digital world by attempting the first ever work done on quantifying human
curiosity, basing itself on various theories drawn from humanities and social
sciences. Curious communication pushes people to spot, read and click the
message from their social feed or any other form of online presentation. Our
approach focuses on measuring the strength of the stimulus to generate reader
curiosity by using unsupervised and supervised machine learning algorithms, but
is also informed by philosophical, psychological, neural and cognitive studies
on this topic. Manually annotated news headlines - clickbaits - have been
selected for the study, which are known to have drawn huge reader response. A
binary classifier was developed based on human curiosity (unlike the work done
so far using words and other linguistic features). Our classifier shows an
accuracy of 97% . This work is part of the research in computational humanities
on digital politics quantifying the emotions of curiosity and outrage on
digital media.
