Interpretable Bangla fake news classification using BERT and traditional machine learning approaches
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022.
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Brac University
2023
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| Онлайн доступ: | http://hdl.handle.net/10361/21830 |
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10361-218302023-10-16T21:04:04Z Interpretable Bangla fake news classification using BERT and traditional machine learning approaches Anan, Ramisa Modhu, Elizabeth Antora Suter, Arjun Sneha, Ifrit Jamal Rasel, Annajiat Alim Abdullah, Matin Saad Mostakim, Moin Department of Computer Science and Engineering, Brac University Bangla fake news Natural language processing BNLP Traditional machine learning BERT Natural language processing (Computer science) Fake news--Prevention--Data processing This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 40-43). Fake news is a type of content that is inaccurate or misleading and it is usually published with the intention of damaging a person or organization’s reputation. It has recently grown significantly in the online forum and on social media platform like Facebook, Reddit, Twitter etc. Because of its falsified statements, people are often persuaded by false news, which has serious consequences in the real world. As a result, there is a growing interest in the field of fake news identification, even though the majority of fake news identification studies are for English language whereas just few of them are for Bangla language. In our study, we come up with a BERT-based system that uses Stratified K-fold cross validation that can achieve 98.45% test accuracy, whereas only the Random Forest can achieve 86.83% accuracy among all the traditional machine learning models. Furthermore, we used Local Interpretable Model-Agnostic Explanations to provide explainability to our system. In this research, we have used the existing BanFakeNews dataset to identify Bangla Fake News. The primary focus of this paper is to develop a model that can recognize fake news in natural language processing so that the developed model can decrease the time it takes individuals to extract fake news from social media. Ramisa Anan Elizabeth Antora Modhu Arjun Suter Ifrit Jamal Sneha B.Sc. in Computer Science 2023-10-16T03:56:58Z 2023-10-16T03:56:58Z ©2022 2022-09-29 Thesis ID 19201101 ID 18301075 ID 18101419 ID 19201136 http://hdl.handle.net/10361/21830 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. 56 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Bangla fake news Natural language processing BNLP Traditional machine learning BERT Natural language processing (Computer science) Fake news--Prevention--Data processing |
| spellingShingle |
Bangla fake news Natural language processing BNLP Traditional machine learning BERT Natural language processing (Computer science) Fake news--Prevention--Data processing Anan, Ramisa Modhu, Elizabeth Antora Suter, Arjun Sneha, Ifrit Jamal Interpretable Bangla fake news classification using BERT and traditional machine learning approaches |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. |
| author2 |
Rasel, Annajiat Alim |
| author_facet |
Rasel, Annajiat Alim Anan, Ramisa Modhu, Elizabeth Antora Suter, Arjun Sneha, Ifrit Jamal |
| format |
Thesis |
| author |
Anan, Ramisa Modhu, Elizabeth Antora Suter, Arjun Sneha, Ifrit Jamal |
| author_sort |
Anan, Ramisa |
| title |
Interpretable Bangla fake news classification using BERT and traditional machine learning approaches |
| title_short |
Interpretable Bangla fake news classification using BERT and traditional machine learning approaches |
| title_full |
Interpretable Bangla fake news classification using BERT and traditional machine learning approaches |
| title_fullStr |
Interpretable Bangla fake news classification using BERT and traditional machine learning approaches |
| title_full_unstemmed |
Interpretable Bangla fake news classification using BERT and traditional machine learning approaches |
| title_sort |
interpretable bangla fake news classification using bert and traditional machine learning approaches |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/21830 |
| work_keys_str_mv |
AT ananramisa interpretablebanglafakenewsclassificationusingbertandtraditionalmachinelearningapproaches AT modhuelizabethantora interpretablebanglafakenewsclassificationusingbertandtraditionalmachinelearningapproaches AT suterarjun interpretablebanglafakenewsclassificationusingbertandtraditionalmachinelearningapproaches AT snehaifritjamal interpretablebanglafakenewsclassificationusingbertandtraditionalmachinelearningapproaches |
| _version_ |
1814306846399266816 |