Detection of Polarizing Narratives in Social Media through Machine Learning during Peruvian Political Unrest
Angel Javier Quispe Carita, Renzo Apaza Cutipa, Juan Carlos Juarez Vargas, Milton Antonio López Cueva, Ernesto Nayer Tumi Figueroa, Fred Torres-Cruz
Abstract
This study analyzes the polarization of narratives on social media during the protests that occurred in Puno, Peru, in 2023, using advanced natural language processing (NLP) and machine learning techniques. 1,378 comments from Facebook and 1,400 from YouTube were collected and preprocessed, applying cleaning, normalization, and stop word removal procedures in Spanish. For sentiment analysis, the VADER model was used, classifying opinions into positive and negative, with an overwhelming predomina
Topics & Concepts
Social mediaNarrativeUnrestArtificial intelligencePoliticsMedia studiesSociologySentiment analysisSocial unrestPolarization (electrochemistry)MicrobloggingComputer scienceSocial movementPolitical scienceNews mediaSocial scienceSentiment Analysis and Opinion MiningMisinformation and Its ImpactsHate Speech and Cyberbullying Detection