With the popularization of the internet, smartphones and social media,
information is being spread quickly and easily way, which implies bigger
traffic of information in the world, but there is a problem that is harming
society with the dissemination of fake news. With a bigger flow of information,
some people are trying to disseminate deceptive information and fake news. The
automatic detection of fake news is a challenging task because to obtain a good
result is necessary to deal with linguistics problems, especially when we are
dealing with languages that not have been comprehensively studied yet, besides
that, some techniques can help to reach a good result when we are dealing with
text data, although, the motivation of detecting this deceptive information it
is in the fact that the people need to know which information is true and
trustful and which one is not. In this work, we present the effect the
pre-processing methods such as lemmatization and stemming have on fake news
classification, for that we designed some classifier models applying different
pre-processing techniques. The results show that the pre-processing step is
important to obtain betters results, the stemming and lemmatization techniques
are interesting methods and need to be more studied to develop techniques
focused on the Portuguese language so we can reach better results.

这项研究重点讨论了自动检测假新闻的挑战性问题，特别关注于处理语言学问题和采用预处理技术（如词形还原和词干提取），结果表明预处理步骤对于获得更好的结果至关重要，词干提取和词形还原等方法是有趣的技术，并且需要更多针对葡萄牙语的研究以获得更好的结果。