Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner
Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9186024/ |
| _version_ | 1819173586696404992 |
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| author | Md. Tawkat Islam Khondaker Junaed Younus Khan Tanvir Alam M. Sohel Rahman |
| author_facet | Md. Tawkat Islam Khondaker Junaed Younus Khan Tanvir Alam M. Sohel Rahman |
| author_sort | Md. Tawkat Islam Khondaker |
| collection | DOAJ |
| description | Authorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an `Agree' and a `Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book. |
| first_indexed | 2024-12-22T20:25:26Z |
| format | Article |
| id | doaj.art-29373b70e9bc42cea65616af16f7e6e7 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T20:25:26Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-29373b70e9bc42cea65616af16f7e6e72022-12-21T18:13:45ZengIEEEIEEE Access2169-35362020-01-01816232216233410.1109/ACCESS.2020.30216589186024Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free MannerMd. Tawkat Islam Khondaker0https://orcid.org/0000-0001-5335-0723Junaed Younus Khan1https://orcid.org/0000-0001-8138-1105Tanvir Alam2https://orcid.org/0000-0001-7033-3693M. Sohel Rahman3https://orcid.org/0000-0001-9419-6478Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, BangladeshDepartment of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, BangladeshCollege of Science and Engineering, Hamad Bin Khalifa University, Doha, QatarDepartment of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, BangladeshAuthorship discrimination is the task of detecting whether two writings are authored by the same person. From literature study to forensic analysis, the authorship discrimination makes a significant contribution in differentiating authorship. In this work, we propose Agree-to-Disagree (A2D), a novel framework for the authorship discrimination task. It is a two-stage deep learning-based framework consisting of an `Agree' and a `Disagree' network. At the first stage, it learns the authorship attributes with its Agree network. Subsequently, through its Disagree network, the framework attempts to differentiate the authorship of a new dataset (completely unrelated to the training dataset), a novel use case that has not been systematically considered hitherto in the literature. We show that A2D is not dependent on the dataset-specific prior knowledge and it can learn only from authorship attributes of the dataset to detect whether two different writings are from the same author. We prove that the A2D framework can successfully reveal the authorship with pseudonyms through tasking it with unfolding the pseudonyms of a famous American short story writer Washington Irving. We also apply our framework on a historical topic of ascertaining whether the authorship of the most respected book in Islam (the Holy Quran) can be attributed to the Prophet of Islam. Through the experimental analysis, A2D reveals that the Prophet of Islam is not the author of the Holy Quran, and this result is in perfect alignment with the belief of 1.8 billion Muslims around the globe regarding the authorship of this holy book.https://ieeexplore.ieee.org/document/9186024/Authorship discriminationdeep learningmachine learningnatural language processingneural networks |
| spellingShingle | Md. Tawkat Islam Khondaker Junaed Younus Khan Tanvir Alam M. Sohel Rahman Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner IEEE Access Authorship discrimination deep learning machine learning natural language processing neural networks |
| title | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_full | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_fullStr | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_full_unstemmed | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_short | Agree-to-Disagree (A2D): A Deep Learning-Based Framework for Authorship Discrimination Task in Corpus-Specificity Free Manner |
| title_sort | agree to disagree a2d a deep learning based framework for authorship discrimination task in corpus specificity free manner |
| topic | Authorship discrimination deep learning machine learning natural language processing neural networks |
| url | https://ieeexplore.ieee.org/document/9186024/ |
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