Machine learning and Neural networks in Fake news detection : A mapping study

Sammanfattning: Fake news, or information disorder, is a societal problem that could be partially remedied by automatic detection tools. While still a young research field many such tools have been proposed in academic writing. This systematic mapping study gives an overview of the current research in Natural Language Process-based fake news detection utilising Machine Learning and Neural Network classification algorithms in regards to which classification algorithms have been studied and which datasets have been used. Furthermore, we attempt to make a generalised description of the performance (measured in f-score and accuracy) of the most commonly occurring classification algorithms. From a corpus of 124 research articles and other scientific texts we identify 63 different datasets mainly written in English, and 116 different classification algorithms. The seven most commonly occurring algorithms (Random Forest, Logistic Regression, Support Vector Machine, Decision Tree, Long Short- TermMemory, K-Nearest Neighbors, Convolutional Neural Network) together make up almost 50% of all algorithm occurences in the article corpus. For these seven, the ten occurrences with the best performance are listed. Out of the datasets, the six most common datasets (ISOT, FakeNewsNet, Patwa 2021, LIAR, Bisaillon, and UTK-MLC) together make up 44% of all dataset occurrences. Apart from English, the represented languages were mainly Chinese (Mandarin), Portugese, Indonesian, Bangla, and Albanian.