Improvement of Misleading and Fake News Classification for Flective Languages by Morphological Group Analysis

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.

Supervised ensemble learning methods towards automatically filtering Urdu fake news within social media

“…For the Urdu language , Adaboost outperforms the other seven machine learning models on a very small corpus. DT improves the classification accuracy for fake news detection for the Slovak language (Kapusta & Obonya, 2020).…”

“…Drawbacks of the DT include overfitting and instability, a complex tree for a highdimensional dataset that is not easy to interpret (Pham et al, 2021). For the fake news detection task, DT has shown good performance for Slovak (Kapusta & Obonya, 2020), Portuguese (Silva et al, 2020), English (Gravanis et al, 2019;Ozbay & Alatas, 2020), and Urdu languages.…”

“…This is an open research question and can be investigated in future studies, (3) we aim to explore the detection performance of ensemble learning techniques. Several recent studies about fake news detection in Slovak (Kapusta & Obonya, 2020), Italian (Fornaciari & Poesio, 2013), and Spanish (Posadas-Durán et al, 2019) used corpora with even less than two thousand news articles (see Table 1).…”

“…Ensemble learning methods have shown good performance in various applications, including solar irradiance prediction (Lee et al, 2020), slope stability analysis (Pham et al, 2021), natural language processing (Sangamnerkar et al, 2020), malware detection (Gupta & Rani, 2020), traffic incident detection (Xiao, 2019). In the past, several studies explored machine learning models for fake news detection task in a few languages like Portuguese (Monteiro et al, 2018;Silva et al, 2020), Spanish (Posadas-Durán et al, 2019;Abonizio et al, 2020), Urdu , Arabic (Alkhair et al, 2019), Slavic (Faustini & Covões, 2020;Kapusta & Obonya, 2020), and English (Kaur, Kumar & Kumaraguru, 2020;Ozbay & Alatas, 2020). As compared to machine learning, a few efforts have been made to explore ensemble learning for fake news detection like Indonesian (Al-Ash & Wibowo, 2018;Al-Ash et al, 2019), English (Kaur, Kumar & Kumaraguru, 2020;Sangamnerkar et al, 2020).…”

The Impact of Term Fake News on the Scientific Community. Scientific Performance and Mapping in Web of Science

“…In this line, there are many investigations that aim to identify and stop the "information hoaxes" that are promoted through social networks. This is a study that works on the development of models that analyze the combinations of lexicon, syntax, and semantic information of the text to determine the veracity of the news (Kapusta and Obonya 2020;Barton 2019;Zakharov et al 2019;Conroy et al 2015).…”