# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
###### 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. fake news; text classification; multi-language; stylometry; machine learning +
Footnote †: journal:  
0000-0001-5000-0001-5000]J. M.  
Traditionally, news was disseminated by influential newspapers and television media only. Today, however, news articles can be written and spread by anyone with access to the Internet. Moreover, OSM promote channels that do not have editorial norms nor content review, which can be a problem since some people might suffer from a lack of critical analysis of news [4]. [PERSON] et al. [5] highlighted as a natural human behavior the absence of concern in verifying the credibility of most of the information given in blogs and virtual encyclopedias, and the natural tendency to believe in the content shared. For example, the 2016 American elections were pervaded by the massive dissemination of information through OSM, which biased the voting decisin Reference [6]. However, not all information was confirmed and verified, which brings us to one of the most severe current issues, the fake news. Fake news is similar in appearance to legitimate news [7] but refers to news articles created to deceive the reader, either for the author benefit or that of a third party, generally involving monetary gain Reference [6]. [PERSON] et al. [8] define the fake news detection problem as a function \(F(a)\), where \(F\) is a prediction function and \(a\) is a news article. Given a news article \(a\), which is described by the _Publish_ and _Content_ set of attributes, \(F\) predicts if \(a\) is a fake news piece or not. Although this approach defines fake news detection as a binary classification, other works have addressed several types of fake news [9; 10; 11]. [PERSON] et al. [9] describe three types of fake news: _serious fabrications_, which are deliberate fraudulent reports, _large-scale hoaxes_, which is another type of falsification that may be mistakenly validated by traditional media, and _humorous fakes_, where readers are aware of the humorous intent. [PERSON] et al. [11] explore the dichotomy between satirical and non-satirical news, while [PERSON] et al. [10] develops a link between deception detection and computational satire, irony, and humor detection.