# Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features
## 5 Conclusions  
The identification of deceptive news is required due to the increase of news consumption through OSM, which provides unruled content broadcasting. Moreover, the spreading speed in OSM requires an automated method to accomplish the detection task. In this paper, we presented a comparison of language-independent features of stylometric, complexity, and psychological types in a multi-language scenario. Furthermore, we proposed stylometric features to improve the identification of satirical, legitimate and fake news articles over three different languages: American English, Brazilian Portuguese, and Spanish. The first contribution of this paper is the creation of a curated  
Figure 7: Features sorted by their normalized importance computed using RF importance. Proposed features are highlighted in bold.  
multi-language corpora composed of news articles from three different classes. Moreover, we made this corpus available for other researchers and practitioners.  
To detect news intent, we presented a text processing pipeline under content-based premises. The preprocessing stage is composed of filtering, cleaning and noise removal phases. Then, _Complexity_, _Stylometric_, and _Psychological_ features are extracted from textual data. Moreover, feature importance was explored toward supporting a suitable predictive ML model using 9930 news articles. Support Vector Machine (SVM), _k_-Nearest Neighbors (_k_-NN), Random Forest (RF), and Extreme Gradient Boosting (XGB) were compared to recommend one that best fit the detection model.  
The RF algorithm reached the highest performance, achieving an average accuracy of 85.3%, followed closely by XGB and SVM, with no statistical difference. The overall performance of ML models indicates that purely stylometric features, including the proposed POS-tag diversity, the ratio of named entities to text size, the ratio of quotation marks to text size, and the OOV words frequency, were capable of enhancing the predictive outcomes, being statistically superior to the other compositions with RF, XGB, and SVM as the most predictive algorithms.  
Besides that, the shared pattern between the studied languages suggests there is an underlying behavior among different languages, which can support fake news detection over several idioms beyond those explored in this work.  
For future works, we will evaluate transfer learning strategies, using pre-trained models to extract more abstract features, such as semantic level characteristics. Either pre-trained word embeddings can be assessed using the multi-language corpora assembled in this paper.  
All the authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This study was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001, Coordination for the National Council for Scientific and Technological Development (CNPq) of Brazil - Grant of Project 420562/2018-4 and Fundacao Araucaria (Parana, Brazil).  
The authors declare no conflict of interest.