Implementation of Machine Learning and Deep Learning Techniques for Fake News Detection: A Novel Approach

DOI

10.2991/978-94-6239-697-5_26How to use a DOI?

Keywords

Optimization; CNN; Classification model; Computation; Deep learning

Abstract

Fake news is the creation of intentional and deceptive information to shape the opinion of people, and the rate at which it has gone viral on social media has caused severe consequences: Social, Political and Economic issues. Consequently, fake news detection has been automatically made a significant field of research. The current research paper shows a comparative analysis of various machine learning and deep learning algorithms to detect fake news using four publicly available datasets: CodaLab, ReCOVery, FARN, and GossipCop. Initially preprocessing of the text is carried out and then feature extraction is to be implement by Bag of Words (BOW), Frequency Inverse Document Frequency (TF-IDF), and Word2Vec. Naive Bayes, Logistic Regression, Random Forest, Support Vector Classifier, and Multilayer Perceptron are some of the classification models that are tested based on accuracy, precision, recall, and F1-values score. It has been experimentally demonstrated that in most cases, frequency-based feature representations are better, perfectly predict semantic embeddings. Specifically, Random Forest coupled with BOW has the best accuracy of 98.8% on the FARN data, and TF-IDF offers stable inter-dataset performance. These observations demonstrate the significance of proper selection of features-classifier in the detection of fake news.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Adarsh Kumar Mishra
AU  - Goldy Attri
AU  - Ankit Sharma
AU  - Kshatrapal Singh
PY  - 2026
DA  - 2026/06/04
TI  - Implementation of Machine Learning and Deep Learning Techniques for Fake News Detection: A Novel Approach
BT  - Proceedings of the Conference on Bridging Engineering Disciplines with AI and Machine Learning (BEDAIML 2026)
PB  - Atlantis Press
SP  - 308
EP  - 322
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-697-5_26
DO  - 10.2991/978-94-6239-697-5_26
ID  - Mishra2026
ER  -