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Uncovering the Truth: A Deep Learning Approach to Detecting Fake News

Bishal Bose*

Department of Computer Science, University of Mumbai, Navi Mumbai, India.

Periodicity:July - December'2023
DOI : https://doi.org/10.26634/jaim.1.2.19771

Abstract

Fake news is a growing problem on social media and can have significant negative consequences for individuals and society owing to its accessibility, low cost, and quick distribution. It is difficult to automatically identify bogus news that defies the current content-based analysis techniques. One of the key reasons is that current NLP algorithms still lack common sense, which is frequently necessary for understanding how to read the news. Recent research has demonstrated that the propagation patterns of true and fake news differ on social media. With the potential for automatic fake news detection, this study investigates the use of Deep Learning (DL) models to detect fake news on social media. A Neural Network (NN) model was developed using Natural Language Toolkit (NLTK), TensorFlow, and Natural Language Processing (NLP) for textual analysis. The model was trained on a dataset of fake and real news articles, and was able to achieve high accuracy in identifying fake news. The results of this study suggest that DL models can be valuable tools for detecting fake news on social media.

Keywords

Artificial Intelligence, Deep Learning (DL), Natural Language Processing (NLP), Text Analysis, Natural Language Toolkit (NLTK), Neural Network (NN).

How to Cite this Article?

Bose, B. (2023). Uncovering the Truth: A Deep Learning Approach to Detecting Fake News. i-manager’s Journal on Artificial Intelligence & Machine Learning, 1(2),1-11. https://doi.org/10.26634/jaim.1.2.19771

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