Beating the news using Social Media: the case study of American Idol
This work demonstrates real-time prediction of opinion formation events using social media data, though it is incremental as it applies existing methods to a simplified case like TV show voting.
The study tackled predicting American Idol contestant eliminations by analyzing Twitter activity during show airings and voting periods, finding that tweet volume correlates with rankings and allows anticipation of outcomes, with geolocation data revealing regional fanbase polarizations.
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.