Social media to predict the 2024 mexican presidential election: a three model approach

Abstract

(Only 1 page) The appearance and rise of social media have evolved the way people interact with each other. From interpersonal communication to mass media production, social media applications have shifted the approach to how an individual or a complete corporation could generate and propagate a message. It was just a matter of time before this new way of reason- ing communication influenced political messages too. Ever since Obama´s 2008 and 2012 victories, the role social media could play in a presidential election was evident. More re- cently, the 2016 Cambridge Analytica scandal ultimately defined how influential the content people see on social media could be. Numerous research has emerged aiming to foresee these political movements based on online performance and many methods have been proposed. The definitive, most common pattern in these works is sentiment analysis in social media posts. The process is simple: detect how many people ’like’ a candidate´s online presence, and how many don´t, and this will likely represent the outcome of an election. However, this approach has sparked both criticism and unsatisfying results. The following work considers a contemporary approach to predicting elections with social media and collected polls. This strategy has succeeded in the case of various Latin American countries such as Argentina, Brazil, Colombia, and Mexico. However, we aim to identify potential flaws and improvements in the method to prove a concrete methodology can work outside a single election exercise, a repetitive cause for concern for multiple experts in the field. Results show that some replicated experiments do not successfully predict the result of the 2024 Mexican presidential election, as in 2018. However, we prove concrete method- ologies and models, like the multi-layer perceptron model (MLP) can successfully predict electoral results in more than one election. Moreover, we propose the least absolute shrink- age and selection operator (LASSO) to construct better and more descriptive predictors for electoral results. These two utilized implementations accurately predicted the winner of the 2024 election but remained short of the official performance of the winning candidate, Claudia Sheinbuam. In the case of the second and third place, both models merely missed the official result by 3 points.