FakeBERT: Fake news detection in social media with a BERT-based deep learning approach

In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researc...

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Vydáno v:Multimedia tools and applications Ročník 80; číslo 8; s. 11765 - 11788
Hlavní autoři: Kaliyar, Rohit Kumar, Goswami, Anurag, Narang, Pratik
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2021
Springer Nature B.V
Témata:
Artificial neural networks
Classification
Coders
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Digital media
Machine learning
Model accuracy
Model testing
Multimedia Information Systems
Neural networks
News
Sentences
Social networks
Special Purpose and Application-Based Systems
Deep learning
BERT
Neural network
Fake news
Social media
ISSN:1380-7501, 1573-7721
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Shrnutí:In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a unidirectional way. Therefore, a bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT. Such a combination is useful to handle ambiguity, which is the greatest challenge to natural language understanding. Classification results demonstrate that our proposed model (FakeBERT) outperforms the existing models with an accuracy of 98.90%.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-10183-2