Work

Unveiling political polarization on Twitter: Machine learning and sentiment analysis in presidential elections

Year: 2024

Type: article

Abstract: The year 2024 stands out as a pivotal year marked by significant political transformations across the globe. Some countries, such as Mexico and the United States, could be deeply affected by political polarization and echo chambers. This study employed sentiment analysis and machine learning techniques to investigate political polarization on Twitter during the 2018 Mexican presidential election. The findings reveal that the winning candidate exhibited the highest level of polarization. This underscores the pivotal role of social media in elections. For some time now, social media platforms like Twitter have contributed to intensified political polarization and the creation of echo chambers. Further research is essential to understand the influence of polarization on voter decision-making and democratic procedures. Establishing ethical guidelines for using machine learning in policy analysis is critical to preserving the integrity of democratic processes while reaping the potential benefits of new technologies.

Source: JeDEM - eJournal of eDemocracy and Open Government

DOI: 10.29379/jedem.v16i1.846

Authors: David Valle-Cruz, Rodrigo Sandoval-Almazán, Asdrúbal López-Chau, J. Ignacio Criado

Institutions: Universidad Autónoma del Estado de México, State University of New York, University at Albany, State University of New York, Universidad Autónoma de Madrid

Language: English

Topic: Social Media and Politics

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

SDG: Peace, justice, and strong institutions

Open Access status: diamond

Funders: European Commission, Consejo Nacional de Ciencia y Tecnología, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Universidad Autónoma de Madrid