scholarly journals Augmentation and heterogeneous graph neural network for AAAI2021-COVID-19 fake news detection

2022 ◽  
Author(s):  
Andrea Stevens Karnyoto ◽  
Chengjie Sun ◽  
Bingquan Liu ◽  
Xiaolong Wang
Keyword(s):  
Neural Network ◽  
Fake News

FNDNet – A deep convolutional neural network for fake news detection

2020 ◽  
Vol 61 ◽  
pp. 32-44 ◽  
Author(s):  
Rohit Kumar Kaliyar ◽  
Anurag Goswami ◽  
Pratik Narang ◽  
Soumendu Sinha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Fake News

scholarly journals Fake News Detection using Deep Learning

2020 ◽  
Vol 9 (9) ◽  
pp. 226-228
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Deep Neural Network ◽  
Negative Impact ◽  
Research Area ◽  
Fake News ◽  
The World

News is a routine in everyone's life. It helps in enhancing the knowledge on what happens around the world. Fake news is a fictional information madeup with the intension to delude and hence the knowledge acquired becomes of no use. As fake news spreads extensively it has a negative impact in the society and so fake news detection has become an emerging research area. The paper deals with a solution to fake news detection using the methods, deep learning and Natural Language Processing. The dataset is trained using deep neural network. The dataset needs to be well formatted before given to the network which is made possible using the technique of Natural Language Processing and thus predicts whether a news is fake or not.

scholarly journals Neural User Response Generator: Fake News Detection with Collective User Intelligence

2018 ◽  
Author(s):  
Feng Qian ◽  
Chengyue Gong ◽  
Karishma Sharma ◽  
Yan Liu
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Semantic Information ◽  
Experimental Results ◽  
News Article ◽  
Fake News ◽  
Additional Information ◽  
Word Level

Fake news on social media is a major challenge and studies have shown that fake news can propagate exponentially quickly in early stages. Therefore, we focus on early detection of fake news, and consider that only news article text is available at the time of detection, since additional information such as user responses and propagation patterns can be obtained only after the news spreads. However, we find historical user responses to previous articles are available and can be treated as soft semantic labels, that enrich the binary label of an article, by providing insights into why the article must be labeled as fake. We propose a novel Two-Level Convolutional Neural Network with User Response Generator (TCNN-URG) where TCNN captures semantic information from article text by representing it at the sentence and word level, and URG learns a generative model of user response to article text from historical user responses which it can use to generate responses to new articles in order to assist fake news detection. We conduct experiments on one available dataset and a larger dataset collected by ourselves. Experimental results show that TCNN-URG outperforms the baselines based on prior approaches that detect fake news from article text alone.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

scholarly journals Capturing the Style of Fake News

2020 ◽  
Vol 34 (01) ◽  
pp. 490-497
Author(s):  
Piotr Przybyla
Keyword(s):  
Neural Network ◽  
High Accuracy ◽  
General Purpose ◽  
Training Data ◽  
Fake News ◽  
Realistic Evaluation ◽  
News Websites ◽  
Text Classifiers ◽  
Automatic Methods ◽  
Online Sources

In this study we aim to explore automatic methods that can detect online documents of low credibility, especially fake news, based on the style they are written in. We show that general-purpose text classifiers, despite seemingly good performance when evaluated simplistically, in fact overfit to sources of documents in training data. In order to achieve a truly style-based prediction, we gather a corpus of 103,219 documents from 223 online sources labelled by media experts, devise realistic evaluation scenarios and design two new classifiers: a neural network and a model based on stylometric features. The evaluation shows that the proposed classifiers maintain high accuracy in case of documents on previously unseen topics (e.g. new events) and from previously unseen sources (e.g. emerging news websites). An analysis of the stylometric model indicates it indeed focuses on sensational and affective vocabulary, known to be typical for fake news.

Fake News Detection in the Framework of Decision-Making System through Graph Neural Network

2021 ◽  
Author(s):  
Ihor Pilkevych ◽  
Dmytro Fedorchuk ◽  
Olena Naumchak ◽  
Mykola Romanchuk
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Fake News ◽  
Decision Making System
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