dc.contributor.author |
Rashid, Mohammad Rifat Ahmmad |
|
dc.contributor.author |
Roy, Rahul |
|
dc.contributor.author |
Rahman, Din M Sumon |
|
dc.contributor.author |
Saleh, Musa Akram |
|
dc.contributor.author |
Khan, Abdul Ali Hayder |
|
dc.contributor.author |
Abu Rayhan, Md. |
|
dc.contributor.author |
Ahmed, Khandaker Foysal |
|
dc.contributor.author |
Monsoor, Nafees |
|
dc.contributor.author |
Hasan, Mahamudul |
|
dc.date.accessioned |
2024-08-24T19:53:43Z |
|
dc.date.available |
2024-08-24T19:53:43Z |
|
dc.date.issued |
2024-08-24 |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5852 |
|
dc.description.abstract |
In an effort to address the growing issue of misinformation on social media, particularly in the context of the Covid-19 pandemic, we have diligently developed a comprehensive dataset on Bangla misinformation. This dataset was scraped from FactWatch, a leading fact-checking organization in Bangladesh, and annotated with fact ratings. It includes a meticulously curated collection of 1014 fact-checked reports spanning from October 4, 2021, to May 25, 2023. These reports encompass a diverse array of summaries, categories, and reliable correctness labels, providing samples of the original fake news content along with investigative descriptions of the fact-checking processes employed. The dataset represents a significant contribution to Bangladesh's participation in the global effort to combat fake news and serves as a crucial resource for ongoing research in misinformation studies, natural language processing, and automated fact-checking, particularly for content in the Bengali language. Addressing the issue of misinformation within the under-researched Bangla language context, our study also leveraged this dataset for deep learning analysis, employing advanced techniques such as Long Short-Term Memory (LSTM) networks and Bidirectional Encoder Representations from Transformers (BERT) with a Bangla base model. The BERT model, with its robust Transformer architecture, excelled in linguistic analysis, achieving an accuracy of 98.77%, while the LSTM model, adept at handling sequential data, recorded an accuracy of 88.92%. The Bangla BERT base model demonstrated exceptional performance in precision, recall, and F1-score, marking a substantial advancement in misinformation detection for the Bangla language. |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Misinformation; Fact-Checking; Social Media Analysis; Natural Language Processing; Long Short-Term Memory (LSTM) |
en_US |
dc.title |
A Comprehensive Dataset and Deep Learning Approach for Misinformation Detection on Social Media in Bangladesh |
en_US |
dc.identifier.doi |
xxxxxx |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1 |
en_US |
dc.pageend |
10 |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authorcountry |
Bangladesh |
en_US |
dc.contributor.authoraffiliation |
East West University |
en_US |
dc.contributor.authoraffiliation |
University of Liberal Arts |
en_US |
dc.contributor.authoraffiliation |
University of Liberal Arts |
en_US |
dc.contributor.authoraffiliation |
East West University |
en_US |
dc.contributor.authoraffiliation |
East West University |
en_US |
dc.contributor.authoraffiliation |
East West University |
en_US |
dc.contributor.authoraffiliation |
East West University |
en_US |
dc.contributor.authoraffiliation |
University of Liberal Arts Bangladesh |
en_US |
dc.contributor.authoraffiliation |
East West University |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |