  Twitter has been proven to be a notable source for predictive modelling on
various domains such as the stock market, the dissemination of diseases or
sports outcomes. However, such a study has not been conducted in football
(soccer) so far. The purpose of this research was to study whether data mined
from Twitter can be used for this purpose. We built a set of predictive models
for the outcome of football games of the English Premier League for a 3 month
period based on tweets and we studied whether these models can overcome
predictive models which use only historical data and simple football
statistics. Moreover, combined models are constructed using both Twitter and
historical data. The final results indicate that data mined from Twitter can
indeed be a useful source for predicting games in the Premier League. The final
Twitter-based model performs significantly better than chance when measured by
Cohen's kappa and is comparable to the model that uses simple statistics and
historical data. Combining both models raises the performance higher than it
was achieved by each individual model. Thereby, this study provides evidence
that Twitter derived features can indeed provide useful information for the
prediction of football (soccer) outcomes.
