Fake news detection in multiple platforms and languages

Abstract The debate around fake news has grown recently because of the potential harm they can have on different fields, being politics one of the most affected. Due to the amount of news being published every day, several studies in computer science have proposed models using machine learning to detect fake news. However, most of these studies focus on news from one language (mostly English) or rely on characteristics of social media-specific platforms (like Twitter or Sina Weibo). Our work proposes to detect fake news using only text features that can be generated regardless of the source platform and are the most independent of the language as possible. We carried out experiments from five datasets, comprising both texts and social media posts, in three language groups: Germanic, Latin, and Slavic, and got competitive results when compared to benchmarks. We compared the results obtained through a custom set of features and with other popular techniques when dealing with natural language processing, such as bag-of-words and Word2Vec.

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