Eliana Providel

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Eliana Providel, Marcelo Mendoza, Sebastián Ruíz


Social networks are used every day to report daily events, although the information published in them many times correspond to fake news. Detecting these fake news has become a research topic that can be approached using deep learning. However, most of the current research on the topic is available only for the English language. When working on fake news detection in other languages, such as Spanish, one of the barriers is the low quantity of labeled datasets available in Spanish. Hence, we explore if it is convenient to translate an English dataset to Spanish using Statistical Machine Translation. We use the translated dataset to evaluate the accuracy of several deep learning architectures and compare the results from the translated dataset and the original dataset in fake news classification. Our results suggest that the approach is feasible, although it requires high-quality translation techniques, such as those found in the translation’s neural-based models.

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Eliana Providel, Marcelo Mendoza


Misleading information spread on social networks is often supported by activists who promote this type of information and bots that amplify their visibility. The need for useful and timely mechanisms of credibility assessment in social media has become increasingly indispensable. Efforts to tackle this problem in Spanish are growing. The last years have witnessed many efforts to develop methods to detect fake news, rumors, stances, and bots on the Spanish social web. This work leads to a systematic review of the literature that relates the efforts to develop this area in the Spanish language. The work identifies pending tasks for this community and challenges that require coordination among the leading investigators on the subject.

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