The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19

TL;DR

This study explores how NLP and data augmentation techniques can significantly enhance neural network accuracy in detecting COVID-19 fake news in Portuguese, addressing the challenge of misinformation verification.

Contribution

It demonstrates the effectiveness of combining NLP and data augmentation to improve neural network performance in fake news detection for Portuguese language content.

Findings

Improved classification accuracy after applying data augmentation.

Neural network performance increased with NLP techniques.

Effective detection of fake news in Portuguese language.

Abstract

This paper aims to present how the application of Natural Language Processing (NLP) and data augmentation techniques can improve the performance of a neural network for better detection of fake news in the Portuguese language. Fake news is one of the main controversies during the growth of the internet in the last decade. Verifying what is fact and what is false has proven to be a difficult task, while the dissemination of false news is much faster, which leads to the need for the creation of tools that, automated, assist in the process of verification of what is fact and what is false. In order to bring a solution, an experiment was developed with neural network using news, real and fake, which were never seen by artificial intelligence (AI). There was a significant performance in the news classification after the application of the mentioned techniques.