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Title: :  Fake News Stance Detection Using Deep Learning Architecture (CNN)
PaperId: :  22721
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-22721
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
SWAMINATHAN BBANNARI AMMAN INSTITUTE OF TECHNOLOGY
VINISHA KBANNARI AMMAN INSTITUTE OF TECHNOLOGY
JEYA BRUNDHA KBANNARI AMMAN INSTITUTE OF TECHNOLOGY

Abstract

Computer Engineering
Fake news detection, text mining, deep learning, Classification, Benchmark model, WELFake dataset, CNN, word embedding, Bidirectional encoder representations from transformer (BERT), convolutional neural network (CNN), Word2vec and Social media
In the contemporary landscape of information dissemination, the proliferation of fake news poses a significant challenge, impacting societal discourse and decision-making processes. Leveraging advancements in deep learning techniques, particularly Convolutional Neural Networks (CNNs), has emerged as a promising approach for discerning the authenticity of news content. This paper introduces a novel two-phase benchmark model, termed WELFake, designed for fake news detection. The first phase involves preprocessing the dataset and validating news content veracity through linguistic features, while the second phase integrates linguistic feature sets with word embedding (WE) and employs a voting classification scheme. To evaluate the efficacy of our approach, we meticulously curate the WELFake dataset comprising approximately 72,000 articles, amalgamating various datasets to ensure unbiased classification outcomes. Experimental results demonstrate that the WELFake model achieves a remarkable classification accuracy of 96.73%, representing a significant enhancement over existing methodologies. Specifically, our model surpasses the accuracy of bidirectional encoder representations from transformer (BERT) by 1.31% and outperforms CNN models by 4.25%. Moreover, comparative analysis with predictive-based approaches utilizing the Word2vec word embedding method showcases an improvement of up to 1.73%. The findings underscore the effectiveness of our frequency-based and focused analysis of writing patterns, affirming the utility of the WELFake model in combating the dissemination of fake news in real-time social media environments. Additionally, the model achieves an overall accuracy of 93%.

Citations

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IJARIIE SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K. "Fake News Stance Detection Using Deep Learning Architecture (CNN)" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 241-248
MLA SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K. "Fake News Stance Detection Using Deep Learning Architecture (CNN)." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 241-248.
APA SWAMINATHAN B, VINISHA K, & JEYA BRUNDHA K. (2024). Fake News Stance Detection Using Deep Learning Architecture (CNN). International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 241-248.
Chicago SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K. "Fake News Stance Detection Using Deep Learning Architecture (CNN)." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 241-248.
Oxford SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K. 'Fake News Stance Detection Using Deep Learning Architecture (CNN)', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 241-248. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Fake_News_Stance_Detection_Using_Deep_Learning_Architecture__CNN__ijariie22721.pdf (Accessed : 03 April 2024).
Harvard SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K. (2024) 'Fake News Stance Detection Using Deep Learning Architecture (CNN)', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 241-248IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Fake_News_Stance_Detection_Using_Deep_Learning_Architecture__CNN__ijariie22721.pdf (Accessed : 03 April 2024)
IEEE SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K, "Fake News Stance Detection Using Deep Learning Architecture (CNN)," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 241-248, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Fake_News_Stance_Detection_Using_Deep_Learning_Architecture__CNN__ijariie22721.pdf [Accessed : 03 April 2024].
Turabian SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K. "Fake News Stance Detection Using Deep Learning Architecture (CNN)." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 (03 April 2024).
Vancouver SWAMINATHAN B, VINISHA K, and JEYA BRUNDHA K. Fake News Stance Detection Using Deep Learning Architecture (CNN). International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : 03 April 2024]; 10(2) : 241-248. Available from: https://ijariie.com/AdminUploadPdf/Fake_News_Stance_Detection_Using_Deep_Learning_Architecture__CNN__ijariie22721.pdf
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