An Exploration of Unreliable News Classification in Brazil and The U.S
This work addresses the spread of unreliable information, a global issue with negative social impacts, by providing a cross-linguistic classification method, but it is incremental as it extends previous findings to a new context.
The study tackled the problem of classifying unreliable news by analyzing differences in writing style between reliable and unreliable sources in Brazilian and U.S. media, finding that some features are universal across languages and using them to build a machine learning classifier.
The propagation of unreliable information is on the rise in many places around the world. This expansion is facilitated by the rapid spread of information and anonymity granted by the Internet. The spread of unreliable information is a wellstudied issue and it is associated with negative social impacts. In a previous work, we have identified significant differences in the structure of news articles from reliable and unreliable sources in the US media. Our goal in this work was to explore such differences in the Brazilian media. We found significant features in two data sets: one with Brazilian news in Portuguese and another one with US news in English. Our results show that features related to the writing style were prominent in both data sets and, despite the language difference, some features have a universal behavior, being significant to both US and Brazilian news articles. Finally, we combined both data sets and used the universal features to build a machine learning classifier to predict the source type of a news article as reliable or unreliable.