A Novel Methodology for Improving Election Poll Prediction Using Time-Aware Polling

Multiple poll forecasting solutions, based on statistics and economic indices, have been proposed over time, but, as we better understand diffusion phenomena, we know that temporal characteristics provide even more uncertainty. As such, current literature is not yet able to define truly reliable models for the evolution of political opinion, marketing preferences, or social unrest. Inspired by micro-scale opinion dynamics, we develop an original time-aware (TA) methodology which is able to improve the prediction of opinion distribution, by modeling opinion as a function which spikes up when opinion is expressed, and slowly dampens down otherwise. After a parametric analysis, we validate our TA method on survey data from the US presidential elections of 2012 and 2016. By comparing our time-aware method (TA) with classic survey averaging (SA), and cumulative vote counting (CC), we find our method is substantially closer to the real election outcomes. On average, we measure that SA is 6.3% off, CC is 5.6% off, while TA is only 1.5% off from the final registered election outcomes; this difference translates into an ≈ 75% prediction improvement of our TA method. As our work falls in line with studies on the microscopic temporal dynamics of social networks, we find evidence of how macroscopic prediction can be improved using time-awareness.