# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 2 Definitions and Related Work  
Recently, there has been an increasing amount of literature on fake news detection tasks. This paper uses three categories for news articles: fake, legitimate, and satirical. The formal definitions used in this work are extracted from [PERSON] et al. [8]:  
**Definition 1** (Fake News). : _Let a news article \(A\), described by a tuple of two features: authenticity and intent. \(A\) is considered fake if the authenticity is verifiable false, and its intent is to mislead the reader, e.g., its content is deceptive._  
**Definition 2** (Legitimate News). : _Let a news article \(A\), described by a tuple of two features: authenticity and intent. \(A\) is considered legitimate if the authenticity is verifiable true, and its intent is to convey authentic information to the reader, e.g., its content is reliable._  
**Definition 3** (Satirical News). : _Let a news article \(A\), described by a tuple of two features: authenticity and intent. \(A\) is considered satirical if the authenticity is verifiable false, and its intent is entertainment-oriented and reveals its deceptiveness to the consumers._  
Several datasets have been proposed to serve as benchmarks. They vary in number of samples, availability of content, and source languages, but there is still no consensus used by most papers of the field. The _LIAR_ dataset, proposed by [PERSON] [19], contains more than 12,000 human-labeled short statements with fine-grained gradations of truthfulness. However, this dataset composed by short statements, making it difficult to use style-based approaches to identify deception as this type of approach requires more content information. Thus, knowledge-based solutions are more fit in this case. [PERSON] et al. [20] and The fake news Challenge [21] published datasets with a similar proposal: given a claim, the system predicts if other statements are mainly agreeing or disagreeing with it. Though such datasets have a considerable amount of samples, the task is beyond the scope of the present study. To overcome this, we focused on datasets with complete news to classify documents into one of the three categories being assessed in this work, selecting equivalent corpora from different languages. [PERSON] and [PERSON] [22] assembled collected news written in English from different sources and made the corpus publicly available. The authors use a classifier based on writing style features to identify the document's class. The set of features takes into account writing characteristics, such as the frequency of grammatical classes and readability measures. However, many features are language-dependent, which limits the method's applicability. We use their proposed corpus in this work, but, due to the low number of instances, it was complemented. The process of samples complementing is described in Section 3.2. [PERSON] et al. [23] explored possible patterns in fake news and its potential relationship to deception and clickbait. For this, the authors proposed a model focused on the theory of detecting false news, where a news story is investigated at the lexical, syntactic, and semantic levels, which differs from approaches that focus on news content. The authors found that the proposed method can overcome the state-of-the-art, in addition to allowing early detection of false news, even when there is some limitation on the content. However, the results obtained did not consider possible divergences in linguistic structures and did not address any language other than English. [PERSON] et al. [24] and [PERSON] et al. [25] developed and made publicly available, respectively, the corpus Fake.Br, written in Portuguese, and FakeNewsCorpusSpanish, written in Spanish. Both works proposed text classifiers that use textual features. However, some features used on those works, such as Bag-of-Words, are language-dependent.