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Abstract
In the past decade we have witnessed the failure of traditional polls in predicting presidential election outcomes across the world. To understand the reasons behind these failures we analyze the raw data of a trusted pollster which failed to predict, along with the rest of the pollsters, the surprising 2019 presidential election in Argentina. Analysis of the raw and re-weighted data from longitudinal surveys performed before and after the elections reveals clear biases related to mis-representation of the population and, most importantly, to social-desirability biases, i.e., the tendency of respondents to hide their intention to vote for controversial candidates. We propose an opinion tracking method based on machine learning models and big-data analytics from social networks that overcomes the limits of traditional polls. This method includes three prediction models based on the loyalty classes of users to candidates, homophily measures and re-weighting scenarios. The model achieves accurate results in the 2019 Argentina elections predicting the overwhelming victory of the candidate Alberto Fernández over the incumbent president Mauricio Macri, while none of the traditional pollsters was able to predict the large gap between them. Beyond predicting political elections, the framework we propose is more general and can be used to discover trends in society, for instance, what people think about economics, education or climate change.
Highlights
Traditional polling methods using random digit dial phone interviews, opt-in samples of online surveys and interactive voice response are failing to predict election outcomes across the world [1,2,3,4]
We focus on the results of the primary presidential election in Argentina on August 2019 and the subsequent presidential election on October 2019, which represents a classic example of a massive failure of the trusted pollsters in predicting a polarized election electorate, which in this case, led to large market collapses in the country, since investors largely bet on the pollster predictions
We show that a cumulative longitudinal analysis tracking users over time performed on the loyalty classes to the candidates considerably improves previous forescasting results obtained in [17], which were based on instantaneous predictions
Summary
Traditional polling methods using random digit dial phone interviews, opt-in samples of online surveys and interactive voice response are failing to predict election outcomes across the world [1,2,3,4]. In view of how the above issues of low response rate, mis-representation and the social desirability bias/lies (which in the case of Elypsis biased more the younger representative) undermined the predictions of the Argentinian primary elections, we search for suitable replacement techniques using sampling methods for the modern era of big-data science and AI. In this scenario, a good candidate to substitute traditional polls is the social network (Twitter in our study) which simultaneously solves the low response rate (millions of people express their political preferences in the microblogging platform) and the social desirability biases.
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