# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 3 Material and Methods
### Text Processing Pipeline
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Type** & **Name** & **Description** & **Reference** \\ \hline
1 & _Complexity_ & words\_per\_sents & Average words per sentence & [22; 32] \\
2 & _Complexity_ & avg\_word\_size & Average word size & [32; 33] \\
3 & _Complexity_ & sentences & Count of sentences & [34] \\
4 & _Complexity_ & trt & Type-Token Ratio (lexical diversity) & [32; 35] \\
5 & _Stylometric_ & pos\_diversity\_ratio & POS-tag diversity & Proposed \\
6 & _Stylometric_ & entities\_ratio & Ratio of Named Entities to text size & Proposed \\
7 & _Stylometric_ & upper\_case & Uppercase letters & [36] \\
8 & _Stylometric_ & oov\_ratio & OOV words frequency & Proposed \\
9 & _Stylometric_ & quotes\_count & Quotation marks count & [22] \\
10 & _Stylometric_ & quotes\_ratio & Ratio of quotation marks to text size & Proposed \\
11 & _Stylometric_ & ratio\_ADJ & ADJ tag frequency & [22] \\
12 & _Stylometric_ & ratio\_ADP & ADP tag frequency & [22; 32] \\
13 & _Stylometric_ & ratio\_ADV & ADV tag frequency & [22] \\
14 & _Stylometric_ & ratio\_DET & DET tag frequency & [22] \\
15 & _Stylometric_ & ratio\_NOUN & NOUN tag frequency & [22; 32] \\
16 & _Stylometric_ & ratio\_PRON & PRON tag frequency & [22; 32] \\
17 & _Stylometric_ & ratio\_PROPN & PROPN tag frequency & [22] \\
18 & _Stylometric_ & ratio\_PUNCT & PUNCT tag frequency & [22] \\
19 & _Stylometric_ & ratio\_SYM & SYM tag frequency & [22] \\
20 & _Stylometric_ & ratio\_VERB & VERB tag frequency & [22; 32] \\
21 & _Psychological_ & polarity & Sentiment polarity & [22; 36] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of extracted features. There are three types of features: Complexity, Stylometric, and Psychological. POS = part of speech; OOV = Out-Of-Vocabulary, ADJ = Adjectives, ADP = Adposition, ADV = Adverbs, DET = Determiner, NOUN = Noun, PRON = Pronoun, PROPN = Personal Pronouns, PUNCT = Punctuation, SYM = Synonyms and VERB = Verb. The Stylometric features take into account more advanced NLP techniques to extract grammatical and semantic characteristics from the text. The Polyglot package was used as a Named Entity Recognition (NER) [39] to detect named entities, counting recognized entities and their ratio to total text size. A similar approach was proposed by [PERSON] et al. [10], but the authors used as a feature to detect absurdity on news. For POS-tagging, the spaCy package was used to label each token. The package has pretrained models for a variety of languages, making it possible to extend this work to other languages. The tags were used to extract a ratio of specific tags (e.g., VERB) to the total amount of tokens in the document. From that, we can assess the POS-tag diversity, i.e., the ratio of different tags present on text to total text size. Along with POS-tag features, the number of quotation marks and uppercase letters are computed, as proposed in Reference [22]. Moreover, we proposed the usage quotation marks frequency as a feature. Furthermore, we proposed the usage of an Out-Of-Vocabulary (OOV) feature, which uses a dictionary of words for a given language and counts the total words that are not found in this set and their frequencies on the text. The hypothesis behind this feature is to capture neologisms, slangs or other kinds of unusual words. To compute this feature, only the words tagged as adjectives, adverbs, verbs, and nouns were considered. Some features were ignored due to its low variability on at least one of the datasets (ratios of AUX, CONJ, SCONJ, CCONJ, CONJ, PART, and INTJ tags to text size). We disregard those which standard deviation was close to zero as non-informative features. The sentiment polarity was extracted using Polyglot [40], a Python package which implements multi-language sentiment lexicon, as described by [PERSON] and [PERSON] [41]. The package implements sentiment polarity extraction for several languages, including the ones evaluated in this study, which makes it possible to extend the application to other languages.