Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158374
Title: Deep learning-based fake news detection
Authors: Chen, Hanzhi
Keywords: Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chen, H. (2022). Deep learning-based fake news detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158374
Abstract: Identifying the truthfulness of news is crucial as it has a great societal impact, and its importance has increased every year since the information age. After the deep learning models were introduced to generate fake news, it become more difficult for a human to identify fake news. Therefore, researchers proposed neural network models to detect fake news but most models only focus on a few datasets. This dissertation evaluates different types of methods on various datasets for overall performance. Furthermore, we discuss the application range of different types of detection methods.
URI: https://hdl.handle.net/10356/158374
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Master_Dissertation_Chen_Hanzhi_amended_Profsigned.pdf
  Restricted Access
2.58 MBAdobe PDFView/Open

Page view(s)

69
Updated on Dec 5, 2022

Download(s)

8
Updated on Dec 5, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.