and his colleagues have put together attention-based models looking at Twitter activity in the run up to the #americanidol decisions. They were able to predict the winner with a pretty high degree of confidence using an extremely simplified model of the Twitter activity. They posted the model to the arXiv a few days ago, and then updated with results from the big vote yesterday. Their updated paper is linked below.
This is both a validation and benchmarking case for quantifying and modeling the #attentioneconomy .
" We have shown that the open source data available on the web can be used to make educated guesses on the outcome of societal events. Speci?cally, we have shown that extremely simple measures quantifying the popularity of the American Idol participants on Twitter strongly correlate with their performances in terms of votes. A post-event analysis shows that the less voted competitors can be identi?ed with reasonable accuracy (Table II) looking at the Twitter data collected during the airing of the show and in the immediately following hours."
Bruno Gonçalves originally shared this post:
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 outc…