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dc.contributor.authorChoudhary, Monika
dc.contributor.authorChouhan, Satyendra Singh
dc.contributor.authorPilli, Emmanuel S.
dc.contributor.authorVipparthi, Santosh Kumar
dc.date.issued2021
dc.identifier.urihttps://thuvienso.hoasen.edu.vn/handle/123456789/13370
dc.description.abstractFake news has become a major concern over the Internet. It influences people directly and should be identified. In the recent years, various Machine Learning (ML) and Deep Learning (DL) based data-driven approaches have been suggested for fake news classification. Most of the ML based approaches use hand-crafted features extracted from input textual content. Moreover, in DL based approaches, an efficient word embedding representation of input data is also a major concern. This paper presents a deep learning framework, BerConvoNet, to classify the given news text into fake or real with minimal error. The presented framework has two main building blocks: a news embedding block (NEB) and a multi-scale feature block (MSFB). NEB uses Bidirectional Encoder Representations from Transformers (BERT) for extracting word embeddings from a news article. Next, these embeddings are fed as an input to MSFB. The MSFB consists of multiple kernels (filters) of varying sizes. It extracts various features from news word embedding. The output of MSFB is fed as an input to a fully connected layer for classification. To validate the performance of BerConvoNet, several experiments have been performed on four benchmark datasets and various performance measures are used to evaluate the results. Furthermore, the ablative experiments with respect to news article embedding, kernel size, and batch size have been carried out to ensure the quality of prediction. Comparative analysis of the presented model is done with other state of the art models. It shows that BerConvoNet outplays other models on various performance metrics.
dc.format11 p. : ill.
dc.language.isoen
dc.publisherElsevier
dc.sourceApplied Soft Computing. Volume 110
dc.subjectDeep learning
dc.subjectFake news
dc.subjectBERT
dc.subjectCNN
dc.subjectAblation study
dc.titleBerConvoNet : a deep learning framework for fake news classification
dc.typeArticle


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Hiển thị biểu ghi dạng vắn tắt