Enhancing Fake News Classification Through DL Models: Encoder-Decoder Architecture with BLSTM for Improved Accuracy
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Date
2024
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Faculty of Engineering and Natural Science
Abstract
In today’s world, the subject of fake news is crucial. It discusses how social media and traditional media spread false stories or misinformation. This effort aims to use deep learning models to increase the accuracy of fake news classification. We investigate the utilization of bidirectional long short-term memory (BLSTM) networks and attention processes in conjunction with encoder-decoder design to boost accuracy
Description
Keywords
BLSTM, DL Model, fake news
Citation
Sumeyra B.P / Enhancing Fake News Classification Through DL Models: Encoder-Decoder Architecture with BLSTM for Improved Accuracy / 2024 / 7M06102 - Computer Science