# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
###### Abstract
Currently, several websites, like Sensacionalista ([[https://www.sensacionalista.com.br/](https://www.sensacionalista.com.br/)]([https://www.sensacionalista.com.br/](https://www.sensacionalista.com.br/))), Actualidad Panamericana ([[https://actualidadpanamericana.com/](https://actualidadpanamericana.com/)]([https://actualidadpanamericana.com/](https://actualidadpanamericana.com/))), and The Daily Discord ([[http://www.dailydiscord.com/](http://www.dailydiscord.com/)]([http://www.dailydiscord.com/](http://www.dailydiscord.com/))), use humor to create satirical news related to some subject in an exaggerated way, making clear to the reader that this information is not legitimate [11]. However, satirical news can be shared on social networks or suspicious sites without its original context, hence creating the possibility of deceiving the most distracted readers [10]. This happens because the satire uses a format very similar to traditional journalism, which, leveraged by an out-of-context sharing, can be confused as a real story [11]. Moreover, there are several specific strands of various languages that compromise automatic detection, for example, misleading news containing partial truthful informatiin Reference [6]. Therefore, it is not an easy task to identify whether a news article is fake, satirical, or legitimate [11; 12]. Approaches on fake news detection can be split into two categories [8]: social context-based and content-based. The social context-based approaches usually analyze the propagation patterns and the diffusion on social networks to identify deceptive content. The content-based approach can be further divided into two types: knowledge-based, which uses knowledge databases to verify the information; and style-based, which extracts writing style and linguistic features to detect deception. The knowledge-based approaches often use public structured knowledge linked data, as described in [PERSON] et al. [13]. Initially, social context-based solutions seem the most adherent to address the problem of fake news spreading in a language-independent way. However, the requirements of meta-information about the structure, path, and distribution pose several disadvantages to these solutions. Indeed, the complexity demanded by this category to support suitable results paves the way to content-based solutions. In recent years, several content-based solutions have been proposed, mainly based on Natural Language Processing (NLP) and Text Processing (Text Mining). From detecting fake news through a pure NLP [14] classifier, to even distinguishing satire from fake news using social networks as seen in Reference [10]. However, one important challenge of fake or satirical news detection is related to the limitation of language-independent written features [15]. Most content-based works, mainly from linguistic approaches, are based on specific languages, such as, English [16], Spanish [11], and Chinese [17]. For instance, [PERSON] et al. [11] analyzed satirical news from the Twitter ([[https://twitter.com/](https://twitter.com/)]([https://twitter.com/](https://twitter.com/))) social network coming from two Spanish-speaking countries: Mexico and Spain. Their goal was to find differences and linguistic similarities of news that indicate sarcasm. Meanwhile, in other NLP tasks, such as Sentiment Analysis, there is an effort to address the challenge of a language-independent approach, as shown in [PERSON] et al. [18]. Thus, to the best of our knowledge, there are no fake news detection techniques, nor satirical ones, that handle more than one language, showcasing the lack of a general approach. Therefore, the main goal of this work is to propose and compare language-independent features to detect news considering three classes: fake, satirical, and legitimate. For that, we built a pipeline using content-based premises to retrieve news style by extracting _Complexity_, _Stylometric_, and _Psychological_ features in Brazilian Portuguese, American English, and Spanish textual data. The remainder of this paper is organized as follows.