Book ChapterDOI
Machine Learning Methods for Fake News Classification
Paweł Ksieniewicz,Michał Choraś,Rafał Kozik,Michał Woźniak +3 more
- pp 332-339
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TLDR
This work focuses on fake news detection in articles published online and on the basis of extensive research it is confirmed that chosen machine learning algorithms can distinguish them from reliable information.Abstract:
The problem of the fake news publication is not new and it already has been reported in ancient ages, but it has started having a huge impact especially on social media users. Such false information should be detected as soon as possible to avoid its negative influence on the readers and in some cases on their decisions, e.g., during the election. Therefore, the methods which can effectively detect fake news are the focus of intense research. This work focuses on fake news detection in articles published online and on the basis of extensive research we confirmed that chosen machine learning algorithms can distinguish them from reliable information.read more
Citations
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Journal ArticleDOI
OPCNN-FAKE: Optimized Convolutional Neural Network for Fake News Detection
TL;DR: In this paper, an optimized Convolutional Neural Network (OPCNN-FAKE) model was proposed to detect fake news, which achieved the best performance for each dataset compared with other models.
Journal ArticleDOI
Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study
Michal Choras,Konstantinos Demestichas,Agata Giełczyk,Álvaro Herrero,Paweł Ksieniewicz,Konstantina Remoundou,Daniel Urda,Michal Wozniak +7 more
TL;DR: In this article, the authors present the present body of knowledge on the application of such intelligent tools in the fight against disinformation, and propose solutions based solely on the work of experts.
Book ChapterDOI
Sentiment Analysis for Fake News Detection by Means of Neural Networks
TL;DR: An innovative solution for fake news detection that utilizes deep learning methods is presented and experiments prove that the proposed approach allows us to achieve promising results.
Book ChapterDOI
Application of the BERT-Based Architecture in Fake News Detection
TL;DR: A hybrid architecture connecting BERT with RNN is presented; the architecture was used to create models for detecting fake news, and the BERT neural network belongs to this type of architectures.
Journal ArticleDOI
“Brave New World” of Fake News: How It Works
TL;DR: The spread of fake news poses a serious threat to democracy and journalism and therefore, it is urgent to understand this phenomenon as discussed by the authors, and therefore it is necessary to understand how fake news finds the ideal tools to thrive in the digital world.
References
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Proceedings ArticleDOI
Information credibility on twitter
TL;DR: There are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.
Journal ArticleDOI
Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: Wang et al. as discussed by the authors presented a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets.
Journal ArticleDOI
The rise of social bots
TL;DR: In this article, the authors discuss the threat posed by today's social bots and how their presence can endanger online ecosystems as well as our society, and how to deal with them.
Journal ArticleDOI
The Rise of Social Bots
TL;DR: In this paper, the authors discuss the characteristics of modern, sophisticated social bots and how their presence can endanger online ecosystems and our society, and review current efforts to detect social bots on Twitter.
Posted Content
Fake News Detection on Social Media: A Data Mining Perspective
TL;DR: This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia.