Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features

Hugo Queiroz Abonizio, Janaina Ignacio de Morais, Gabriel Marques Tavares and Sylvio Barbon Junior
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Hugo Queiroz Abonizio: State University of Londrina (UEL), 86057-970 Londrina, Brazil
Janaina Ignacio de Morais: State University of Londrina (UEL), 86057-970 Londrina, Brazil
Gabriel Marques Tavares: Università degli Studi di Milano (UNIMI), 20122 Milan, Italy
Sylvio Barbon Junior: State University of Londrina (UEL), 86057-970 Londrina, Brazil

Future Internet, 2020, vol. 12, issue 5, 1-18

Abstract: Online Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure. The majority of studies on automatic fake news detection are restricted to English documents, with few works evaluating other languages, and none comparing language-independent characteristics. Moreover, the spreading of deceptive news tends to be a worldwide problem; therefore, this work evaluates textual features that are not tied to a specific language when describing textual data for detecting news. Corpora of news written in American English, Brazilian Portuguese, and Spanish were explored to study complexity, stylometric, and psychological text features. The extracted features support the detection of fake, legitimate, and satirical news. We compared four machine learning algorithms (k-Nearest Neighbors ( k -NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) to induce the detection model. Results show our proposed language-independent features are successful in describing fake, satirical, and legitimate news across three different languages, with an average detection accuracy of 85.3% with RF.

Keywords: fake news; text classification; multi-language; stylometry; machine learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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