Article: Fake content detection on benchmark dataset using various deep learning models Journal: International Journal of Computational Science and Engineering (IJCSE) 2024 Vol.27 No.5 pp.570 - 581 Abstract: The widespread use of social media and its development have offered a medium for the propagation of fake contents quickly among the masses. Fake contents frequently misguide individuals and lead to erroneous social judgments. Individuals and society have been harmed by the dissemination of low-quality news content on social media. In this paper, we have worked on a benchmark dataset of news content and proposed an approach comprising basic natural language processing techniques with different deep learning models for categorising content as real or fake. Different deep learning models employed are LSTM, bi-LSTM, LSTM and bi-LSTM with an attention mechanism. We compared the outcomes by using one hot word embedding and pre-trained GloVe technique. On benchmark LIAR dataset, the LSTM achieved a better accuracy of 67.2%, while the bi-LSTM with GloVe word embedding reached an accuracy of 67%. An accuracy of 98.22% is achieved using bi-LSTM and 97.98% using LSTM on Real-Fake dataset. Fake news can be a menace to society, so if it is detected early, harmony can be maintained in society and individuals can avoid being misled. Inderscience Publishers - linking academia, business and industry through research

Title: Fake content detection on benchmark dataset using various deep learning models

Authors: Chetana Thaokar; Jitendra Kumar Rout; Himansu Das; Minakhi Rout

Addresses: School of Computer Engineering, KIIT University, Bhubaneswar, India; Department of Information Technology, Ramdeobaba College of Engineering and Management, Nagpur, India ' Department of Computer Science and Engineering, National Institute of Technology Raipur, Raipur, India ' School of Computer Engineering, KIIT University, Bhubaneswar, India ' School of Computer Engineering, KIIT University, Bhubaneswar, India

Abstract: The widespread use of social media and its development have offered a medium for the propagation of fake contents quickly among the masses. Fake contents frequently misguide individuals and lead to erroneous social judgments. Individuals and society have been harmed by the dissemination of low-quality news content on social media. In this paper, we have worked on a benchmark dataset of news content and proposed an approach comprising basic natural language processing techniques with different deep learning models for categorising content as real or fake. Different deep learning models employed are LSTM, bi-LSTM, LSTM and bi-LSTM with an attention mechanism. We compared the outcomes by using one hot word embedding and pre-trained GloVe technique. On benchmark LIAR dataset, the LSTM achieved a better accuracy of 67.2%, while the bi-LSTM with GloVe word embedding reached an accuracy of 67%. An accuracy of 98.22% is achieved using bi-LSTM and 97.98% using LSTM on Real-Fake dataset. Fake news can be a menace to society, so if it is detected early, harmony can be maintained in society and individuals can avoid being misled.

Keywords: fake news; word embedding; global vectors; GloVe; LIAR dataset; deep learning models; bidirectional encoder representations from transformer; BERT.

DOI: 10.1504/IJCSE.2024.141341

International Journal of Computational Science and Engineering, 2024 Vol.27 No.5, pp.570 - 581

Received: 17 Oct 2022
Accepted: 25 May 2023
Published online: 09 Sep 2024 *

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Title: Fake content detection on benchmark dataset using various deep learning models

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