Automatic Fake News Detection for Romanian Online News

• Published in: Information (Basel) 2022-03, Vol.13 (3), p.151
• Main Authors: Buzea, Marius Cristian, Trausan-Matu, Stefan, Rebedea, Traian
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Classification; Coders; convolutional neural network; Data mining; Datasets; Deep learning; Experiments; fake news; False information; Information sources; Language; Machine learning; Natural language processing; Neural networks; News; Recurrent neural networks; RoBERT; Sentiment analysis; Social networks; Support vector machines
• ISSN: 2078-2489; 2078-2489
• DOI: 10.3390/info13030151

This paper proposes a supervised machine learning system to detect fake news in online sources published in Romanian. Additionally, this work presents a comparison of the obtained results by using recurrent neural networks based on long short-term memory and gated recurrent unit cells, a convolutional neural network, and a Bidirectional Encoder Representations from Transformers (BERT) model, namely RoBERT, a pre-trained Romanian BERT model. The deep learning architectures are compared with the results achieved by two classical classification algorithms: Naïve Bayes and Support Vector Machine. The proposed approach is based on a Romanian news corpus containing 25,841 true news items and 13,064 fake news items. The best result is over 98.20%, achieved by the convolutional neural network, which outperforms the standard classification methods and the BERT models. Moreover, based on irony detection and sentiment analysis systems, additional details are revealed about the irony phenomenon and sentiment analysis field which are used to tackle fake news challenges.