The textual information is increasing in the internet through different social network platforms like Twitter, Facebook, Reviews sites, Blogs and discussion Forums etc. These platforms changed the way of communication among people. Most of the people are exchanging genuine information in these platforms. Some of them create false information and spread this information in these platforms. The fake or false information is spreading to defame the reputation of people, companies, products, services and places. The detection of fake news becomes a popular research area in recent times. Most of the researchers proposed several approaches to detect the fake news or false information by analysing the written text. The PAN competition organizers introduced a task of fake news spreaders detection in 2020. The task is detecting whether the news is received from fake news spreader or not. The organizers provided Twitter dataset for fake news spreaders detection. This task is a type of author profiling that is used to predict the demographic information of authors by analysing the text of authors. The Stylometric analysis proved in author profiling domain to distinguish the author writing styles as well as to improve the profiles prediction accuracy. In this work, a set of stylistic features are identified by analysing the dataset to differentiate the writing styles among fake news spreaders and real news spreaders. Different machine learning algorithms are used to generate the classification model for classifying the fake news spreaders. It was identified that the proposed stylistic features based approach attained good accuracies for fake news spreaders detection compared to various approaches in fake news spreaders detection.