# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 3 Material and Methods
### Text Processing Pipeline  
Figure 1 shows an overview of the pipeline composed of three steps: Preprocessing, Feature Extraction, and Classification. The pipeline covers the handling of raw news until the detection outcome. Considering a news article has different sources, the proposed features were carefully created to avoid capturing metadata characteristics, e.g., the amount of spacing, web template, and specific publisher style. It is important to highlight the feature vector is composed by our language-independent proposed features together with traditional descriptors from fake news detection literature. In the first step, Preprocessing (1), a set of procedures are performed to eliminate unwanted characteristics left by the data acquisition phase. The Cleaning phase converts all samples into UTF-8 encoding format and removes non-textual characters, such as _emojis_ and special characters. This manipulation clears website metadata and leaves only news-related textual data. Then, in the Filtering phase, small texts are filtered out from samples to avoid news that are too short. Such process creates a homogeneity in text lengths, listed as a requirement for fake news detectiin Reference [9]. Lastly, the Noise Removal phase removes extra whitespaces, as well as normalizes quotes characters and other collecting and processing related noise that are not related to the news content. Figure 1: Overview of pipeline to detect news articles among fake, legitimate, and satirical using language-independent features  
After preprocessing the textual data, the news articles have their features computed in the Feature Extraction (2) step. The proposed features are language-independent, i.e., they do not consider specific terms from a language. Instead, the features focus on capturing high-level structures. This way, the same set of features can be used in multi-language domains. To achieve this goal, we extracted features from three categories, based on [PERSON] and [PERSON] [22] categorization: Complexity, Stylistic, and Psychological. Complexity-based features aim at capturing the overall intricacy of the news, both in the sentence and word level. For that, we used metrics, such as average word size, words count per sentence, and Type-Token Ratio. Stylistic features use NLP techniques to extract grammatical information from each document, understanding its syntax and text style. Hence, part of speech (POS) tagger is used to track different word style frequencies. Psychological features are mostly related to cognitive processes. For that, we evaluated sentiment polarity [31], which measures the negativity or positivity of a text. Table 1 lists all features used in this paper. Some features have been proposed by other works, but they were never evaluated in a multi-language setup. Thus, we decided to include them in our feature vector to investigate their applicability in scenarios with more than one language. For Complexity features, documents were split into sentences and tokens through a tokenization process. Thus, we can extract the average words per sentence, average word size and the total number of sentences. Complexity metrics are inspired by readability indexes, such as Simple Measure of Gobbledygook (SMOG) Grade [37] and Automated Readability Index [38], which use word and sentence level measures to quantify a reading difficulty score. Type-Token Ratio (TTR) is also extracted by counting the number of unique words divided by the total number of words, measuring the vocabulary variation of the document. Those textual statistics are intended to help the characterization of the complexity of differences between news classes. Word and sentence level features are explored in the literature, as seen in Reference [8; 22; 24; 32; 33].