Analysis of Classifiers for Fake News Detection

As time flows, the amount of data, especially text data increases exponentially. Along with the data, our understanding of AI also increases and the computing power enables us to train very complex and large models faster. Fake news has been gathering a lot of attention worldwide recently. The effects can be political, economic, organizational, or even personal. This paper discusses the approach of natural language processing and machine learning in order to solve this problem. Use of bag-of-words, n-grams, count vectorizer has been made, TF-IDF, and trained the data on five classifiers to investigate which of them works well for this specific dataset of labelled news statements. The precision, recall and f1 scores help us determine which model works best.