Fake news detection exploiting TF-IDF vectorization with ensemble learning models
SK Mondal, JP Sahoo, J Wang, K Mondal… - Advances in Distributed …, 2022 - Springer
Advances in Distributed Computing and Machine Learning: Proceedings of ICADCML …, 2022•Springer
Nowadays, fake news is one of the utmost problems in society. Aspect such as, this paper
presents a basic concepts and principles in the field of fake news detection. The idea and
principles of detection explores the types of detection methods. Besides, based on machine
learning and linguistics, an empirical analysis of fake news detection is explored. In fine, we
elaborate the challenges of fake news detection along with a couple of machine learning
and ensemble learning techniques exploiting the Count and TF-IDF vectorization methods.
presents a basic concepts and principles in the field of fake news detection. The idea and
principles of detection explores the types of detection methods. Besides, based on machine
learning and linguistics, an empirical analysis of fake news detection is explored. In fine, we
elaborate the challenges of fake news detection along with a couple of machine learning
and ensemble learning techniques exploiting the Count and TF-IDF vectorization methods.
Abstract
Nowadays, fake news is one of the utmost problems in society. Aspect such as, this paper presents a basic concepts and principles in the field of fake news detection. The idea and principles of detection explores the types of detection methods. Besides, based on machine learning and linguistics, an empirical analysis of fake news detection is explored. In fine, we elaborate the challenges of fake news detection along with a couple of machine learning and ensemble learning techniques exploiting the Count and TF-IDF vectorization methods.
Springer
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