Analyses of Methods for Prediction of Elections Using Software Systems

The primary objective of this research study is to review and analyze the published literature regarding the possibilities of forecasting and predicting the result of elections using software systems. The factors motivating research institutions and individuals to consider research impact on prediction of elections are manifold. Understanding the impact of different software tools, algorithms and social networking software applications on prediction of elections is a vital, and often overlooked, element of forecasting the election results. The literature review was conducted to examine methods and current software applications and practices as well as projects on election predictions. The review focused in particular on social media applications and different methods on accessing the opinion of the potential voters. The review draws on an international literature, although it is limited to English language publications. The findings identify the different methods used, the advantages and disadvantages of different approaches and the methods that are used currently and that have shown most effective results and recommendations are provided

Forecasting elections results via the voter model with stubborn nodes

In this paper we propose a novel method to forecast the result of elections using only official results of previous ones. It is based on the voter model with stubborn nodes and uses theoretical results developed in a previous work of ours. We look at popular vote shares for the Conservative and Labour parties in the UK and the Republican and Democrat parties in the US. We are able to perform time-evolving estimates of the model parameters and use these to forecast the vote shares for each party in any election. We obtain a mean absolute error of 4.74\%. As a side product, our parameters estimates provide meaningful insight on the political landscape, informing us on the proportion of voters that are strong supporters of each of the considered parties

Forecasting elections results via the voter model with stubborn nodes

15 pagesInternational audienceWe explore a method to influence or even control the diversity of opinions within a polarised social group. We leverage the voter model in which users hold binary opinions and repeatedly update their beliefs based on others they connect with. Stubborn agents who never change their minds (\zealots") are also disseminated through the network, which is modelled by a connected graph. Building on earlier results, we provide a closed-form expression for the average opinion of the group at equilibrium. This leads us to a strategy to inject zealots into a polarised network in order to shift the average opinion towards any target value. We account for the possible presence of a backfire effect, which may lead the group to react negatively and reinforce its level of polarisation in response. Our results are supported by numerical experiments on synthetic data