A hybrid rumor detection model derived from a comparative study of supervised approaches

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Detalhes bibliográficos
Principais autores:	Aothoi, Mehzabin Sadat, Ahsan, Samin, Ahmed, Fardeen
Outros Autores:	Rasel, Mr. Annajiat Alim
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2023
Assuntos:	Rumor detection NLP Machine learning Deep learning Decision tree Random forest Naive bayes Support Vector Machine (SVM) BERT RNN CNN Artificial intelligence Cognitive learning theory
Acesso em linha:	http://hdl.handle.net/10361/20156

id	10361-20156
record_format	dspace
spelling	10361-201562023-08-29T21:02:47Z A hybrid rumor detection model derived from a comparative study of supervised approaches Aothoi, Mehzabin Sadat Ahsan, Samin Ahmed, Fardeen Rasel, Mr. Annajiat Alim Choudhury, Ms. Najeefa Nikhat Department of Computer Science and Engineering, Brac University Rumor detection NLP Machine learning Deep learning Decision tree Random forest Naive bayes Support Vector Machine (SVM) BERT RNN CNN Artificial intelligence Machine learning Cognitive learning theory This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-41). In the current age of social media, information spreads like wildfire. Unfortunately, this also means that misinformation or rumors can spread easily. The spread of this misinformation can have negative consequences for society. This is especially true in recent years due to growing engagement in social media platforms for news. Hence, to prevent the spread of rumors, rumor detection is necessary. Bangladesh has been no exception to the spread of misinformation, causing countless propaganda over the years. Although a significant amount of work has already been conducted regarding rumor detection in English, Bangla rumor detection is still in its infancy. For our research, we first compared several Machine Learning (ML) models and Deep Learning (DL) models for rumor detection using both Bangla and English datasets. Comparing and analyzing the results, we implemented an Ensemble ML model and finally our hybrid model, which is a combination of our best-performing ML model and DL model that outperformed all other baseline state-of-the-art models. Mehzabin Sadat Aothoi Samin Ahsan Fardeen Ahmed B. Computer Science and Engineering 2023-08-29T09:23:11Z 2023-08-29T09:23:11Z 2023 2023-01 Thesis ID: 19101353 ID: 19101497 ID: 22241037 http://hdl.handle.net/10361/20156 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 41 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Rumor detection NLP Machine learning Deep learning Decision tree Random forest Naive bayes Support Vector Machine (SVM) BERT RNN CNN Artificial intelligence Machine learning Cognitive learning theory
spellingShingle	Rumor detection NLP Machine learning Deep learning Decision tree Random forest Naive bayes Support Vector Machine (SVM) BERT RNN CNN Artificial intelligence Machine learning Cognitive learning theory Aothoi, Mehzabin Sadat Ahsan, Samin Ahmed, Fardeen A hybrid rumor detection model derived from a comparative study of supervised approaches
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Rasel, Mr. Annajiat Alim
author_facet	Rasel, Mr. Annajiat Alim Aothoi, Mehzabin Sadat Ahsan, Samin Ahmed, Fardeen
format	Thesis
author	Aothoi, Mehzabin Sadat Ahsan, Samin Ahmed, Fardeen
author_sort	Aothoi, Mehzabin Sadat
title	A hybrid rumor detection model derived from a comparative study of supervised approaches
title_short	A hybrid rumor detection model derived from a comparative study of supervised approaches
title_full	A hybrid rumor detection model derived from a comparative study of supervised approaches
title_fullStr	A hybrid rumor detection model derived from a comparative study of supervised approaches
title_full_unstemmed	A hybrid rumor detection model derived from a comparative study of supervised approaches
title_sort	hybrid rumor detection model derived from a comparative study of supervised approaches
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/20156
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_version_	1814309142971547648

A hybrid rumor detection model derived from a comparative study of supervised approaches

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