Abstract
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.
Highlights
Over the recent years, artificial intelligence (AI) brought important changes in the domain of information technologies and architectures, such as using and developing intelligent transportation systems, virtual personal assistants, robotic surgery, and maybe with the greatest impact on our lives, natural language processing [1].Nowadays, due to the internet, the quality and quantity of the news increases every day, and the way that the consumer accesses and manages daily online information is constantly changing
This paper focuses on analyzing the performance of several models for fake news detection in Romanian by using neural network architectures such as long short-term memory (LSTM), a convolutional neural network (CNN), gated recurrent units (GRU), Bidirectional Encoder Representations from Transformers (BERT), and standard classifiers such as Support Vector Machine (SVM) and Naïve Bayes (NB)
This paper proposes a method that receives as input pre-processed news to reduce the chances of underfitting or overfitting and performs several analyses and transformations, using the term frequency-inverse document frequency (TF-IDF) for feature extraction
Summary
Artificial intelligence (AI) brought important changes in the domain of information technologies and architectures, such as using and developing intelligent transportation systems, virtual personal assistants, robotic surgery, and maybe with the greatest impact on our lives, natural language processing [1]. Fake news can function as propaganda or misinformation, but it always appeals to the emotions of the public and the intent to cover rational responses, analysis, and comparison of information from several sources, encouraging inflammation and outrage and can lead to conspiracy theories and partisan biased content that negatively affects social security. There are other ways to address misinformation such as conspiracies, discrediting, emotion, or social media feeds that may influence democratic processes [6]. In this paper, a statistical analysis for a dataset of online articles with real and fake classes is presented. A sentiment analysis and irony detection systems are applied to our datasets, providing important information in the process of detecting fake news.
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