Uncovering the Truth: Exploring Traditional Deep Learning Techniques for Fabricated News Detection

The identification of fabricated news is becoming increasingly critical in this time of social media and the internet, where it can propagate fast and cause significant damage. Recent research has focused on NLP and ML methods to detect fake news. This research study investigates traditional deep le...

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Bibliographic Details
Published in:	2023 2nd International Conference on Edge Computing and Applications (ICECAA) pp. 714 - 723
Main Authors:	Surya Bharadwaj, G Hari, Manikanta, N S, Bishi, Manas Ranjan, Krishna Teja, P Siva, Rama Koteswara Rao, G, Srinivas, P V V S
Format:	Conference Proceeding
Language:	English
Published:	IEEE 19-07-2023
Subjects:	benchmark datasets Deep learning deep learning techniques Fake news detection Lightning Logistic regression machine learning Recurrent neural networks Reproducibility of results Semantics Social networking (online)
Online Access:	Get full text
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Summary:	The identification of fabricated news is becoming increasingly critical in this time of social media and the internet, where it can propagate fast and cause significant damage. Recent research has focused on NLP and ML methods to detect fake news. This research study investigates traditional deep learning techniques such as Lightning Module, Logistic Regression, LSTM, Word Embedding, RNN, and Bag of Ngrams for detecting Fabricated news. The working of these techniques is evaluated on several benchmark datasets, and their accuracy and efficiency are compared. Moreover, the approach towards prediction only by the news articles is discussed. Results indicate that the approach using these techniques can effectively detect fake news with high accuracy, providing a promising solution to this complex problem. This study aims to achieve the improvement of more advanced and effective techniques for Identifying fake news, helping to mitigate its side effects on society.
DOI:	10.1109/ICECAA58104.2023.10212337