# Correcting public opinion trends through Bayesian data assimilation

arXiv: https://arxiv.org/abs/2105.14276 · HTML: https://mlsanity.com/paper/2105.14276/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 29 May 2021.

## Builds on (most cited first, 3 of 3)

- [Validation of Twitter opinion trends with national polling aggregates: Hillary Clinton vs Donald Trump](https://mlsanity.com/paper/1610.01587/) (2016)
- [Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections](https://mlsanity.com/paper/1901.07933/) (2019)
- [Introduction to the Kalman Filter and Tuning its Statistics for Near Optimal Estimates and Cramer Rao Bound](https://mlsanity.com/paper/1503.04313/) (2015)

## Abstract (arXiv)

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.