Search results
Fake News Detection. 161 papers with code • 9 benchmarks • 27 datasets. Fake News Detection is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake.
Mar 14, 2023 · In order to mitigate the damage caused by fake news, researchers have been seeking the development of automated mechanisms to detect them, such as algorithms based on machine learning as well...
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 30, 2022 · In this paper, we propose a novel deep neural framework for fake news detection. We identify and address two unique challenges related to fake news: (1) early fake news detection and (2) label shortage. The framework has three essential parts: (1) news module, (2) social contexts module and (3) detection module.
Sep 1, 2019 · In this paper, we shall present a novel fake news detection model, FaNDeR(Fake News Detection model using media Reliability) which can efficiently classify the level of truth for...
Jun 10, 2021 · This tutorial aims to clearly present (1) fake news detection problems, challenges, and research direction; (2) a comparison between fake news and other related concepts (e.g., rumors);...
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.
Apr 1, 2021 · The recent achievements of deep learning techniques in complex natural language processing tasks, make them a promising solution for fake news detection too. This work proposes a novel hybrid deep learning model that combines convolutional and recurrent neural networks for fake news classification.
Mar 1, 2021 · This paper makes an analysis of the research related to fake news detection and explores the traditional machine learning models to choose the best, in order to create a model of a product with supervised machine learning algorithm, that can classify fake news as true or false, by using tools like python scikit-learn, NLP for textual analysis.
Fake news is invalid and misleading information that is conveyed as accurate news. Fake news detection has become indispensable in modern society because of the.
