Abstract
Online news has been the majority of people’s information source in recent decades. However, a lot of the information that is accessible online is fake and sometimes even designed to mislead. It might be difficult for individuals to distinguish between certain false newspaper items and the real ones since they are so similar. Deep learning (DL) and machine learning (ML) models, among other automated false news detection (FND) techniques, are quickly becoming essential. A comparative study was conducted to analyze the performance of five prominent deep learning models across four distinct datasets, namely ISOT, FakeNewsNet, Dataset1, and Dataset2. Results indicated that while LSTM achieved the highest accuracy on the ISOT dataset (99.95%), CNN-GRU stood out with an exceptional 99.97% accuracy on the FakeNewsNet dataset. Both CNN-LSTM and LSTM exhibited almost perfect accuracies on Dataset1. On Dataset2, LSTM led with 98.64%. However, LSTM AutoEncoder consistently demonstrated lower efficiency, with accuracies spanning between 49% and 62%. The study underscores the critical role of dataset-specific model selection in optimizing deep learning outcomes.
Recommended Citation
Al-obaidi, Saja A. and Çağlıkantar, Tuba
(2024)
"Automated Fake News Detection System,"
Iraqi Journal for Computer Science and Mathematics: Vol. 5:
Iss.
4, Article 2.
DOI: https://doi.org/10.52866/2788-7421.1200
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol5/iss4/2
