Abstract
Fake news is a phenomenon which is having a significant impact on our social life, in particular in the political world. Fake news detection is an emerging research area which is gaining interest but involved some challenges due to the limited amount of resources (i.e., datasets, published literature) available. We propose in this paper, a fake news detection model that use n-gram analysis and machine learning techniques. We investigate and compare two different features extraction techniques and six different machine classification techniques. Experimental evaluation yields the best performance using Term Frequency-Inverted Document Frequency (TF-IDF) as feature extraction technique, and Linear Support Vector Machine (LSVM) as a classifier, with an accuracy of 92%.
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Ahmed, H., Traore, I., Saad, S. (2017). Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques. In: Traore, I., Woungang, I., Awad, A. (eds) Intelligent, Secure, and Dependable Systems in Distributed and Cloud Environments. ISDDC 2017. Lecture Notes in Computer Science(), vol 10618. Springer, Cham. https://doi.org/10.1007/978-3-319-69155-8_9
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DOI: https://doi.org/10.1007/978-3-319-69155-8_9
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