Implementation of a Big Data Ecosystem for Interdisciplinary Analysis in Academic Projects: Collaboration between Engineering and Communication in the Study of the 2024 Mexican Elections

This article presents the development and application of a Big Data ecosystem at FES Aragón, UNAM, framed within a PAPIIT Project. It details the collaboration with the Electoral Observatory of the Communication program at the same Faculty for analyzing political communication on social networks during the 2024 election campaigns in Mexico. Through massive data processing methodologies, which included comparisons between conventional tools like Python and distributed platforms on a Hadoop cluster and the use of Spark, large volumes of data were structured and cleaned. This methodology allowed for processing information for subsequent comprehensive qualitative and quantitative analysis from the perspectives of ethos, pathos, and logos in the discourse of the candidates participating in the electoral process. The results revealed that, while Python is useful for small datasets, Hadoop and Spark offer superior efficiency in large-scale data processing, highlighting the importance of choosing the appropriate infrastructure for each task. The findings demonstrate the value of the intersection between computer engineering and communication in social and political research, setting a precedent for future interdisciplinary collaborations.