Abstract
Dissemination of fake news and disinformation on social media platforms pose a serious threat to society. Distinguishing between fake and truthful information is not an easy task for humans as well and automatic detection of fake news has received considerable attention in recent years. In this paper, we focus on the task of automatic detection of fake news using several machine learning algorithms. The impact of various linguistic features and preprocessing techniques on the performance of the classifiers has been evaluated using a dataset containing 17324 news entries. The experimental results are encouraging, with the most successful models obtaining accuracy of 99.97%.
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