@InProceedings{celli-EtAl:2016:PEOPLES,
  author    = {Celli, Fabio  and  Stepanov, Evgeny  and  Poesio, Massimo  and  Riccardi, Giuseppe},
  title     = {Predicting Brexit: Classifying Agreement is Better than Sentiment and Pollsters},
  booktitle = {Proceedings of the Workshop on Computational Modeling of People's Opinions, Personality, and Emotions in Social Media (PEOPLES)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {110--118},
  abstract  = {On June 23rd 2016, UK held the referendum which ratified the exit from the EU.
	While most of the traditional pollsters failed to forecast the final vote,
	there were online systems that hit the result with high accuracy using opinion
	mining techniques and big data. Starting one month before, we collected and
	monitored millions of posts about the referendum from social media
	conversations, and exploited Natural Language Processing techniques to predict
	the referendum outcome. In this paper we discuss the methods used by
	traditional pollsters and compare it to the predictions based on different
	opinion mining techniques. We find that opinion mining based on
	agreement/disagreement classification works better than opinion mining based on
	polarity classification in the forecast of the referendum outcome.},
  url       = {http://aclweb.org/anthology/W16-4312}
}