International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

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

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
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

Explainable misinformation detection across multiple social media platform

Author(s) Dhannina Srinivasa Santha Kumar, N Ganga Devi
Country India
Abstract Social media platforms like Facebook, Social Media, and Instagram have revolutionized communication and interaction, but they have also facilitated the rise of Misinformation, particularly among young users. Misinformation, a form of psychological abuse, can lead to severe mental health issues, including anxiety, depression, and even suicide. Detecting and preventing Misinformation on platforms like Social Media remains a significant challenge due to the dynamic nature of tweets, comments etc..., the use of slang, emojis, and sarcasm, and the anonymity provided to perpetrators. Traditional machine learning and deep learning approaches often struggle to adapt to the continuously evolving content of social media messages.
To address these limitations, we propose a novel hybrid approach named DEA-RNN for automated Misinformation detection. DEA-RNN combines Elman-type Recurrent Neural Networks (RNN) with an enhanced Dolphin Echolocation Algorithm (DEA) to optimize network parameters effectively. This approach leverages the strengths of both classifiers and topic modelling, ensuring greater adaptability and improved accuracy in detecting Misinformation instances. Experimental results demonstrate that DEA-RNN outperforms existing models across various evaluation metrics, making it a promising solution for real-time Misinformation detection on Social Media. This study highlights the importance of integrating advanced deep learning models for combating the growing threat of Misinformation on social media platforms
Keywords Misinformation detection, DEA-RNN , social media
Field Computer > Data / Information
Published In Volume 16, Issue 1, January-March 2025
Published On 2025-03-30
Cite This Explainable misinformation detection across multiple social media platform - Dhannina Srinivasa Santha Kumar, N Ganga Devi - IJSAT Volume 16, Issue 1, January-March 2025. DOI 10.71097/IJSAT.v16.i1.3064
DOI https://doi.org/10.71097/IJSAT.v16.i1.3064
Short DOI https://doi.org/g899gk
View / Download PDF File

Share this



CrossRef

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

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

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 IJSAT
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 Message on WhatsApp +91-9687-182-185 editor@ijsat.org