Title: Quantifying Filter Bubbles: Analyzing Surprise in Elections,
Abstract: This work analyses surprising elections, and attempts to quantify the notion
of surprise in elections. A voter is surprised if their estimate of the winner
(assumed to be based on a combination of the preferences of their social
connections and popular media predictions) is different from the true winner. A
voter's social connections are assumed to consist of contacts on social media
and geographically proximate people. We propose a simple mathematical model for
combining the global information (traditional media) as well as the local
information (local neighbourhood) of a voter in the case of a two-candidate
election. We show that an unbiased, influential media can nullify the effect of
filter bubbles and result in a less surprised populace. Surprisingly, an
influential media source biased towards the winners of the election also
results in a less surprised populace. Our model shows that elections will be
unsurprising for all of the voters with a high probability under certain
assumptions on the social connection model in the presence of an influential,
unbiased traditional media source. Our experiments with the UK-EU referendum
(popularly known as Brexit) dataset support our theoretical predictions. Since
surprising elections can lead to significant economic movements, it is a
worthwhile endeavour to figure out the causes of surprising elections.