Next Article in Journal
Influence of Composition and Technological Factors on Variatropic Efficiency and Constructive Quality Coefficients of Lightweight Vibro-Centrifuged Concrete with Alkalized Mixing Water
Next Article in Special Issue
NASca and NASes: Two Monolingual Pre-Trained Models for Abstractive Summarization in Catalan and Spanish
Previous Article in Journal
Comparison of Casein Phosphopeptide with Potassium Nitrate and Sodium Monofluorophosphate Desensitizing Efficacy after In-Office Vital Bleaching—A Randomized Trial
Previous Article in Special Issue
A Corpus-Based Study of Linguistic Deception in Spanish

Ternion: An Autonomous Model for Fake News Detection

Department of Computer Science, Iqra University, Karachi 76400, Pakistan
College of Computer Science and Information Systems, Najran University, Najran 61441, Saudi Arabia
Department of Computer Science, NED University of Engineering and Technology, Karachi 76400, Pakistan
Authors to whom correspondence should be addressed.
Appl. Sci. 2021, 11(19), 9292;
Received: 22 August 2021 / Revised: 22 September 2021 / Accepted: 27 September 2021 / Published: 6 October 2021
(This article belongs to the Special Issue Current Approaches and Applications in Natural Language Processing)
In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%. View Full-Text
Keywords: fake news detection; natural language processing; machine learning; stance detection; social media fake news detection; natural language processing; machine learning; stance detection; social media
Show Figures

Figure 1

MDPI and ACS Style

Islam, N.; Shaikh, A.; Qaiser, A.; Asiri, Y.; Almakdi, S.; Sulaiman, A.; Moazzam, V.; Babar, S.A. Ternion: An Autonomous Model for Fake News Detection. Appl. Sci. 2021, 11, 9292.

AMA Style

Islam N, Shaikh A, Qaiser A, Asiri Y, Almakdi S, Sulaiman A, Moazzam V, Babar SA. Ternion: An Autonomous Model for Fake News Detection. Applied Sciences. 2021; 11(19):9292.

Chicago/Turabian Style

Islam, Noman, Asadullah Shaikh, Asma Qaiser, Yousef Asiri, Sultan Almakdi, Adel Sulaiman, Verdah Moazzam, and Syeda Aiman Babar. 2021. "Ternion: An Autonomous Model for Fake News Detection" Applied Sciences 11, no. 19: 9292.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop