Abstract

Due to the constantly evolving social media and different types of sources of information, we are facing different fake news and different types of misinformation. Currently, we are working on a project to identify applicable methods for identifying fake news for floating language types. We explored different approaches to detect fake news in the presented research, which are based on morphological analysis. This is one of the basic components of natural language processing. The aim of the article is to find out whether it is possible to improve the methods of dataset preparation based on morphological analysis. We collected our own and unique dataset, which consisted of articles from verified publishers and articles from news portals that are known as the publishers of fake and misleading news. Articles were in the Slovak language, which belongs to the floating types of languages. We explored different approaches in this article to the dataset preparation based on morphological analysis. The prepared datasets were the input data for creating the classifier of fake and real news. We selected decision trees for classification. The evaluation of the success of two different methods of preparation was carried out because of the success of the created classifier. We found a suitable dataset pre-processing technique by morphological group analysis. This technique could be used for improving fake news classification.

Highlights

  • Nowadays, the Internet is part of our daily lives, and, at the same time, it is one of the main sources of information for the users

  • We decision trees to verify the suitability of using tags

  • We compared the accuracythe of decision trees methods

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Summary

Introduction

The Internet is part of our daily lives, and, at the same time, it is one of the main sources of information for the users. Because of social networks or media, we are facing various fake news over the entire Internet. The entire fake news model requires an extensive amount of time and a relevant elaborate dataset [1,2]. More and more new messages and articles are emerging over the Internet. The fake news is a phenomenon that relates to various topics, which is continuously read by many users. This effect is very favorable for those who wrote these fake news [3]. Fake news is an important area because there are many explanations and theories why people believe in fake news, and there are various approaches on how to detect them [4,5]

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