Forecasting opinion trends from real-time social media is the long-standing goal of modern-day big-data analytics. Despite its importance, there has been no conclusive scientific evidence so far that social media activity can capture the opinion of the general population at large. Here we develop analytic tools combining statistical physics of complex networks, percolation theory, natural language processing and machine learning classification to infer the opinion of Twitter users regarding the candidates of the 2016 US Presidential Election. Using a large-scale dataset of 73 million tweets collected from June 1st to September 1st 2016, we investigate the temporal social networks formed by the interactions among millions of Twitter users. We infer the support of each user to the presidential candidates and show that the resulting Twitter trends follow the New York Times National Polling Average, which represents an aggregate of hundreds of independent traditional polls, with remarkable accuracy ($r=0.9$). More importantly, the Twitter opinion trend forecasts the aggregated NYT polls by 6 to 15 days, showing that Twitter can be an early warning signal of global opinion trends at the national level. Our analytics [available at \url{kcorelab.com}] unleash the power of Twitter to predict social trends from elections, brands to political movements, and at a fraction of the cost of national polls. UPDATE November 7, 2016: We apply our analysis to the final part of the presidential election. Our Twitter opinion trend anticipates The Upshot NYT polling average by up to 13 crucial days and shows Clinton as the winner of the Twitter opinion by 55.5\% versus Trump 44.5\% of the votes (normalized to two candidates).
['Alexandre Bovet', 'Flaviano Morone', 'Hernán A. Makse']
Predicting election trends with Twitter: Hillary Clinton versus Donald Trump