Multimodal fake news detection using text and image
Date
2023-05
Journal Title
Journal ISSN
Volume Title
Publisher
BRAC University
Abstract
Development in information and technology has made the communication easier
in the recent decades. Easy access of social media is creating restraints amid of
differentiating fake and real news. In the recent period the problem has increased
drastically and use of image is making the news more impactful. Even though news
websites are publishing the news and provide the source of authentication still there
are other portals and platform which intentionally spread fake news to exploit an
event. In this paper we proposed a hybrid system where we are combining CNN
and RNN to detect fake news . We applied two techniques to reduce the model
complexity and increase accuracy based on text data and image. With this system,
detecting fake news it’ll stop misleading people and creating an unstable situation
as well as taking benefits of the situation.
Description
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 58-59).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
Includes bibliographical references (pages 58-59).
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
Keywords
Natural language processing, Multinomial naive bayes, BERT, Bi-GRU, Bi-LSTM, Word embedding, Fake news, Support vector machine
