Machine learning for detecting fake news

With the development of communication technologies and social media, the fake news phenomena is expanding quickly. A new field of research that is receiving a lot of attention is fake news detection. Due to the restricted resources, including datasets, processing, and analysis methods, it does, nevertheless, confront some difficulties. In this paper, we presented a mode for identifying bogus news based on machine learning. We used Support Vector Machine (SVM) as a classifier and term frequency-inverse document frequency (TF-IDF) of a set of words and n-grams as a feature extraction approach. We also propose a dataset of authentic and fake news for the training of the proposed system. The outcomes show how efficient the system is.