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With the popularization of the internet, smartphones and social media,information is being spread quickly and easily way, which implies biggertraffic of information in the world, but there is a problem that is harmingsociety with the dissemination of fake news. With a bigger flow of information,some people are trying to disseminate deceptive information and fake news. Theautomatic detection of fake news is a challenging task because to obtain a goodresult is necessary to deal with linguistics problems, especially when we aredealing with languages that not have been comprehensively studied yet, besidesthat, some techniques can help to reach a good result when we are dealing withtext data, although, the motivation of detecting this deceptive information itis in the fact that the people need to know which information is true andtrustful and which one is not. In this work, we present the effect thepre-processing methods such as lemmatization and stemming have on fake newsclassification, for that we designed some classifier models applying differentpre-processing techniques. The results show that the pre-processing step isimportant to obtain betters results, the stemming and lemmatization techniquesare interesting methods and need to be more studied to develop techniquesfocused on the Portuguese language so we can reach better results.

The effect of stemming and lemmatization on Portuguese fake news text classification