Call for Papers:Vol.12 Issue.1

Submission
Last date		 28-Feb-2026
Acceptance Status In One Day
Paper Publish In Two Days
Submit ManuScript

News & Updates

Submit Article

Dear Authors, Article publish in our journal for Volume-12,Issue-1. For article submission on below link: Submit Manuscript


Join As Board

Dear Reviewer, You can join our Reviewer team without given any charges in our journal. Submit Details on below link: Join As Board


Paper Publication Charges

IJARIIE APP
Download Android App

For Authors

Archives

IJARIIE Board

Downloads

Android App

Download IJARIIE APP

Authors

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

Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.

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
BibTex EndNote RefMan RefWorks

Number Of Downloads


Last download on 4/3/2024 9:49:54 AM

Save in Google Drive

Similar-Paper

TitleArea of ResearchAuther NameAction
Enhancing Historical Consciousness through Digital Storytelling among Grade 11 General Academic Strand Students of Purisima National High School”SOCIAL SCIENCECHARITY P. CANONIGO Download
Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning,TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPyInformation Technology / Computer Engineering / Machine LearningRoshani Rajesh khobragade Download
WEALTH: YOUR SMART EXPENSE TRACKER AND FINANCIAL ADVISORInformation TechnologyPratiksha Mankar Download
SUVIDHA An E-commerce website for Local LaboursInformation Technology Aman Milind Mandape Download
Comprehensive Framework for Real-Time Hand Gesture Recognition on Mobile Platforms using Machine Learning, TensorFlow Lite, Keras, MediaPipe, OpenCV and NumPyInformation Technology / Computer Engineering / Machine LearningRoshani Rajesh khobragade Download
EDUNEXUS – A Student Development ApplicationInformation TechnologyNamrata Kanhole Download
Review on Festagram: An Event Management System for CollegesInformation science and engineeringAdarsh Download
Deep Learning Based 3D Object Detection and Pose EstimationComputer EngineeringPayal Behune Download
AI-Based Dynamic Route Generation and Optimization Using Real-Time Traffic and Road ParametersInformation Technology EngineeringAniket Ubale Download
SMART EXPIRY DATE TRACKER WITH FOOD RECOMMENDATIONSInformation Science and EngineeringMr Pradeep Nayak Download
A REVIEW ON ABSTRACTIVE MEETING SUMMARIZATION: TOWARDS SMARTER MEETING ASSISTANTSInformation Science and EngineeringKratika Sudarshan Halankar Download
A Review on Smart Appointment Scheduling Systems for Modern Hospital ManagementInformation Science and EngineeringDivya S Nayak Download
A Comprehensive Review of AI-Powered Resume Screening and Analysis SystemsInformation Science and EngineeringMr NAGESH U B Download
Stock Data Prediction and Visualization using Advanced Learning and Dash FrameworkInformation on Science and EngineeringMr Mounesh Download
BLOOD BANK AND DONOR MANAGEMENT SYSTEMInformation Science and Engineering Mr. Mounesh A Download
12