International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2025) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
May-June 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Fake News Detection

Author(s) Ms. Deepthi Rani S S, Anuroop Prasad
Country India
Abstract Recent years have seen a significant and widespread rise in false news, which is defined as material that is shared with the intention of defrauding people.This kind of misinformation is dangerous to social cohesion and wellbeing because it exacerbates political polarisation and public mistrust of authority figures.As a result, fake news is a phenomena that significantly affects our social lives, especially in politics.In order to address this issue, this study suggests brand-new methods based on machine learning (ML) and deep learning (DL) for the fake news identification system.This paper's primary goal is to identify the best model that produces high accuracy performance.Hence, in order to identify fake news, we provide an improved Convolutional Neural Network model (OPCNN-FAKE).Using four benchmark datasets for fake news, we assess how well OPCNN-FAKE performs in comparison to Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and The Six Regular ML Techniques: Decision Tree (DT), logistic Regression (LR), K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB). The parameters of ML and DL have each been optimised using the grid search and hyperopt optimization approaches, respectively. Moreover, Glove word embedding has been utilised to encode features as a feature matrix for DL models while N-gram and Term FrequencyInverse Document Frequency (TF-IDF) have been used to extract features from the benchmark datasets for regular ML. Accuracy, precision, recall, and F1- measure were used to validate the data in order to assess the performance of the OPCNN-FAKE. Compared to other models, the OPCNN-FAKE model has the best performance for each dataset.
Keywords Convolutional Neural Network model (OPCNN-FAKE),K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and Naive Bayes (NB)
Field Computer Applications
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-21
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.45182
Short DOI https://doi.org/g9mh6s
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 3Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com