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Oct 26, 2022 · Fake News Detection using Machine Learning. Last Updated : 26 Oct, 2022. Fake news on different platforms is spreading widely and is a matter of serious concern, as it causes social wars and permanent breakage of the bonds established among people. A lot of research is already going on focused on the classification of fake news.
Machine learning algorithms used for fake news detection can be divided into two main categories: supervised and unsupervised learning. Supervised learning algorithms are trained on labelled datasets, where each news article is labelled as either real or fake.
there is a need for machine learning classifiers that can detect these fake news automatically. Use of machine learning classifiers for detecting the fake news is described in this systematic literature review. Keywords: Online fake news, Machine learning, fake news, Text Classification, social media
In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency-inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier.
Jan 1, 2022 · dEFEND (Shu et al., 2019a) models news content and news comments using a deep co-attention method to detect fake news. dEFEND simultaneously chooses the most significant news sentences and comments for interpretability.
Oct 17, 2020 · In this paper, we propose a solution to the fake news detection problem using the machine learning ensemble approach. Our study explores different textual properties that could be used to distinguish fake contents from real.
Apr 14, 2023 · Content-Based Fake News Detection (CBFND) has the purpose of assessing news intention as a set of quantifiable features, often machine learning features, extracted from news content. CBFND is a critical tool for identifying news harmfulness.
