No 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.

Nowadays, education is a complex process that has many advantages. This is obvi-ously proven, as there are high demands on skills in today’s world. Therefore, it is a good approach to acquire this knowledge during the studies. Therefore, the re-quirement is aimed at the constantly improving and acquiring new experiences. In order to meet as many of these parameters as possible, it is important that we have an appropriately structured environment for students. The teaching process can be interpreted in several ways. In our research, we focus mainly on teaching through e-learning systems. Obviously, these supporting systems have many advanced func-tionalities to help make the whole learning process much easier to understand. In our work, we focus on methods and approaches by which we can evaluate student be-haviour and we can measure the justified course settings. We explored various man-agerial settings inside a concrete course structure. Subsequently there will be statistical evaluation of already cleaned and preprocessed data from the system. At the same time, based on these statistical confirmations, we can propose a set of methodologi-cal recommendations for the teacher, which will help us to improve the quality and effectiveness of the teaching process.

No abstract

No abstract