Covid-19 Fake News Detection Model On Social Media Data Using Machine Learning Techniques - Liew, Kelvin Kai Xuan (2023)
Social media sites like Instagram, Twitter and Facebook have become an indispensible part of the daily routine. These social media sites are powerful instruments for spreading news, photographs, and other sorts of information. However, since the emergence of the COVID-19 pandemic in December 2019, many articles and headlines concerning the COVID-19 epidemic have surfaced on social media. Social media is frequently used to disseminate fraudulent material or information. This disinformation may confuse consumers, perhaps causing worry. It is hard to counter the widespread dissemination of disinformation. As a result, it is critical to develop a model for recognising fakes news in the news stream. The dataset, which would be a synthesis of COVID-19-related news from numerous social media and news sources, is utilised for categorization in this work. Markers are retrieved from unstructured textual data gathered from a variety of sources. Then, to eliminate the computational burden of analysing all of the features in the dataset, feature selection is done. Finally, to categorise the covid -19 related dataset, multiple cutting-edge machine learning algorithms were trained. Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT) are the machine learning models presented. Finally, numerous measures are used to evaluate these algorithms.