Beating the news using Social Media: the case study of American Idol

TL;DR

This study demonstrates that Twitter activity and geolocation data can predict American Idol voting outcomes and reveal regional fanbase distributions, illustrating the potential of social media analytics for forecasting societal events.

Contribution

It introduces a method to predict voting results and map fanbases using Twitter data, highlighting social media's role in real-time opinion analysis.

Findings

Twitter activity correlates with contestant rankings

Geolocation data reveals regional fanbase polarization

Social media can anticipate voting outcomes

Abstract

We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it, correlates with the contestants ranking and allows the anticipation of the voting outcome. Furthermore, the fraction of Tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. Although American Idol voting is just a minimal and simplified version of complex societal phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators anticipating the future unfolding of opinion formation events.