Unmasking False News in the Twitterverse: Precision-Optimized Classification Algorithms for Verifying Information in Peru

Abstract

The Internet has the problem known as the spread of fake news. Based on this, the authors have chosen the social network Twitter to be studied because it is known as the medium in which false news is frequently disseminated. Therefore, this investigation elaborates on a data set using natural language processing. It comprises 1600 tweets classified as true or false according to their content and based on news verification articles from Perú. With this data set, four classification models are designed with high precision to identify if a tweet is true or false, using first Natural Language Processing, Logistic Regression, Support Vector Machine, Dense Neural Network, and Random Forests algorithms. Then, the hyperparameters of all algorithms are tuned. Finally, after the performance evaluation of the classification models, the authors recommend the Support Vector Machine as the best algorithm.