# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 2 Definitions and Related Work
Another difference between those works and ours is that the former treats the problem as a binary classification (legitimate and fake), while we treat as a multi-class problem (legitimate, fake, and satirical), understanding that satirical news is a separate type of document. [PERSON] et al. [26] proposed a Decision Support System (DSS) based on a multi-label text classification pipeline for news into two conceptual classes: objective/satirical and legitimate/fake. For this, the authors used a Portuguese dataset collected from Brazilian sites and considered the stylistic features from the news in DSS. As a result, news can be categorized as objective and legitimate, satirical and fake, or any other combination of those two classes at the same time. Nonetheless, the work is limited to a Portuguese dataset with features proposed and evaluated only on the Portuguese language structure. Different from Reference [26], in this work, we considered different languages and evaluated the extracted features in a language-independent setup. [PERSON] and [PERSON] [27] and [PERSON] et al. [28] focused on identifying fake news spread directly from social networks, specifically Twitter in those cases. The usage of Deep Learning (DL) methods is also present on the field, where [PERSON] et al. [16] applied a Long Short Term Memory (LSTM) model to obtain gradual authenticity score of the news. However, DL methods usually depend on large amounts of data to extract patterns. Therefore, traditional ML methods fit better on real scenarios, where data availability is limited. [PERSON] et al. [29] collected datasets in two languages (Portuguese and English) and analyzed the similarities in stylometry in both languages, based on the search for universal characteristics that are independent of culture, or specific attributes to each language. The authors found that the attributes of unreliable articles follow a similar pattern in both languages, suggesting the existence of stylistic characteristics when separating reliable and unreliable articles in both languages. However, the results were restricted to only two languages, not enough to conclude if stylometric patterns are observed in multiple languages. In addition, the authors only inspect the reliability of the articles, without necessarily verifying the veracity of the information or analyzing other characteristics, such as satire. [PERSON] et al. [30] addressed a dataset composed of both French and English to distinguish fake, trusted and satire contents, but, different from our proposal, their data representation remain language-dependent, since it relies on specific languages pretraining methods like word vectors or term frequencies. Therefore, given the lack of study on fake news detection on languages other than English, a comparison between different languages is one of the main contributions of this work. Moreover, previous studies have evaluated only fake and legitimate news, while we leveraged a more broad scenario by considering fake, legitimate, and satirical news. Instead of adhering to a corpus collected on a single language, three corpora of distinct languages (English, Portuguese, and Spanish) were used to conduct the experiments. The models were induced in each corpus, evaluated, and the most important features were compared by analyzing the characteristics shared among languages. The features used in this paper are carefully proposed and chosen to be language-independent, in order to test the same set of features on different corpora idioms, increasing the applicability of our method.