  Measuring public opinion is a key focus during democratic elections, enabling
candidates to gauge their popularity and alter their campaign strategies
accordingly. Traditional survey polling remains the most popular estimation
technique, despite its cost and time intensity, measurement errors, lack of
real-time capabilities and lagged representation of public opinion. In recent
years, Twitter opinion mining has attempted to combat these issues. Despite
achieving promising results, it experiences its own set of shortcomings such as
an unrepresentative sample population and a lack of long term stability. This
paper aims to merge data from both these techniques using Bayesian data
assimilation to arrive at a more accurate estimate of true public opinion for
the Brexit referendum. This paper demonstrates the effectiveness of the
proposed approach using Twitter opinion data and survey data from trusted
pollsters. Firstly, the possible existence of a time gap of 16 days between the
two data sets is identified. This gap is subsequently incorporated into a
proposed assimilation architecture. This method was found to adequately
incorporate information from both sources and measure a strong upward trend in
Leave support leading up to the Brexit referendum. The proposed technique
provides useful estimates of true opinion, which is essential to future opinion
measurement and forecasting research.
