# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 3 Material and Methods
### News Datasets  
The experiments conducted in this study include documents originally written in American English (EN), Brazilian Portuguese (PT), and Spanish (ES). For that, we created a corpus composed of news articles proposed in other works and complemented them when necessary. Namely, we used: the corpus of [PERSON] [22] for EN news; the Fake.Br corpus [24] for PT news; and the FakeNewsCorpusSpanish corpus [25] for ES news. Note that Reference [24; 25] used only fake and legitimate classes, without considering the satirical class. As the original corpora had an uneven distribution of classes (fake, satirical, and legitimate), we augmented them in an effort to standardize the news collection used in our experiments. To achieve this goal, we increased the number of samples by adding documents extracted from the Fake News Corpus (FNC) FNC is an open source dataset composed of millions of news articles collected on OpenSources ), a list of credible and non-credible online sources. The list uses several tags to label documents. We considered documents tagged as _Fake News_, _Satire_, and _Credible_ categories, which correspond to fake, satirical, and legitimate classes, respectively. Moreover, FNC contain news articles written in both ES and EN. To detect the language employed in the document, we used the Python package _whatthelang_[42]. Since the FakeNewsCorpusSpanish lacks satirical news, we complemented this collection with FNC samples. For that, we selected ES samples with the _Satire_ label from FNC. Regarding the EN collection, the corpus from Reference [22] consists of only 326 documents. To complement it, FNC documents of _Fake News_, _Satire_, and _Credible_ were added to the EN corpus. The FNC does not include news written in PT. Thus, to overcome this gap, following the collecting method in Reference [26],we extracted news from two widely known Brazilian satirical portals: Sensacionalista and Diario Pernambucano. Therefore, the corpora used our work is backed both by literature and open source community datasets. Lastly, due to classes imbalance, sampling was made in order to keep the same number of documents for each class in order to avoid the class imbalance problem [43; 44]. The experimentation was carried out using 9930 news articles split into three datasets grouped by language. As shown on Table 2, the EN corpus contains 2043 samples of each class, for a total of 6129 documents, while the PT corpus contains 846 samples of each class, totaling 2538 documents, and ES contains 421 samples for each class and 1263 in total. The EN corpus has 4,432,906 tokens, of which 94,496 are unique (2.1%), and an average of 723 tokens per document. While the PT corpus contains 1,246,924 tokens, of which 58,129 are unique (4.6%), with an average of 491 tokens per document. Lastly, the ES corpus contains a total of 459,406 tokens with 40,891 of them being unique (8.9%), with an average of 364 tokens per document. Table 3 shows examples for each class on each language. The complete dataset is made publicly available for other researchers and practitioner ([[http://www.uel.br/grupo-pesquisa/remid/?page_id=145](http://www.uel.br/grupo-pesquisa/remid/?page_id=145)]([http://www.uel.br/grupo-pesquisa/remid/?page_id=145](http://www.uel.br/grupo-pesquisa/remid/?page_id=145))). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Corpus** & **Samples** & **Samples per Class** & **Tokens** & **Unique Tokens** & **References** \\ \hline
**English (EN)** & 6129 & 2043 & 4,432,906 & 94,496 & [22], FNC \\
**Portuguese (PT)** & 2538 & 846 & 1,246,924 & 58,129 & [24], Sensacionalista, Diario Pernambucano \\
**Spanish (ES)** & 1263 & 421 & 459,406 & 40,891 & [25], FNC \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Corpora information summarising characteristics for English, Portuguese, and Spanish datasets. FNC = Fake News Corpus.