论文标题
使用数据增强作为提高神经网络准确性的技术，以检测有关COVID-19的假新闻
The use of Data Augmentation as a technique for improving neural network accuracy in detecting fake news about COVID-19
论文作者
论文摘要
本文旨在介绍如何应用自然语言处理（NLP）和数据增强技术如何改善神经网络的性能，以更好地检测葡萄牙语的假新闻。假新闻是过去十年来互联网增长期间的主要争议之一。验证事实和错误的事实证明是一项艰巨的任务，而虚假新闻的传播速度更快，这导致需要创建工具，这些工具，自动化，有助于验证事实和错误的验证过程。为了携带解决方案，使用新闻，真实和假的，通过神经网络开发了一个实验，人工智能（AI）从未见过。在应用上述技术后，新闻分类表现出色。
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.
