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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
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.
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.
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.
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.
This paper reviews various Machine learning approaches in detection of fake and fabricated news. The limitation of such and approaches and improvisation by way of implementing deep learning is also reviewed.
Explore and run machine learning code with Kaggle Notebooks | Using data from Fake News Detection.
