Quantifying Filter Bubbles: Analyzing Surprise in Elections

Sagar Massand, Swaprava Nath

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.

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