FNDNet – A deep convolutional neural network for fake news detection

•Fake News Detection using A Deep Neural Network.•Experimentation using Deep Learning Algorithms with hyper-parameters optimization.•Achieved 98.36% accuracy in Fake News Detection. With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Cognitive systems research Ročník 61; s. 32 - 44
Hlavní autoři: Kaliyar, Rohit Kumar, Goswami, Anurag, Narang, Pratik, Sinha, Soumendu
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.06.2020
Témata:
Deep learning
Fake news
Machine learning
Neural network
Social media
Deep learning
Neural network
Fake news
Social media
Machine learning
ISSN:1389-0417, 1389-0417
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:•Fake News Detection using A Deep Neural Network.•Experimentation using Deep Learning Algorithms with hyper-parameters optimization.•Achieved 98.36% accuracy in Fake News Detection. With the increasing popularity of social media and web-based forums, the distribution of fake news has become a major threat to various sectors and agencies. This has abated trust in the media, leaving readers in a state of perplexity. There exists an enormous assemblage of research on the theme of Artificial Intelligence (AI) strategies for fake news detection. In the past, much of the focus has been given on classifying online reviews and freely accessible online social networking-based posts. In this work, we propose a deep convolutional neural network (FNDNet) for fake news detection. Instead of relying on hand-crafted features, our model (FNDNet) is designed to automatically learn the discriminatory features for fake news classification through multiple hidden layers built in the deep neural network. We create a deep Convolutional Neural Network (CNN) to extract several features at each layer. We compare the performance of the proposed approach with several baseline models. Benchmarked datasets were used to train and test the model, and the proposed model achieved state-of-the-art results with an accuracy of 98.36% on the test data. Various performance evaluation parameters such as Wilcoxon, false positive, true negative, precision, recall, F1, and accuracy, etc. were used to validate the results. These results demonstrate significant improvements in the area of fake news detection as compared to existing state-of-the-art results and affirm the potential of our approach for classifying fake news on social media. This research will assist researchers in broadening the understanding of the applicability of CNN-based deep models for fake news detection.
ISSN:1389-0417
1389-0417
DOI:10.1016/j.cogsys.2019.12.005