Volume 15, Issue 1 (Special Issue on AI in ICT 2023)                   2023, 15(1): 63-71 | Back to browse issues page

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Farhoodi M, Toloie Eshlaghi A, Motadel M. The Effect of Data Augmentation Techniques on Persian Stance Detection. International Journal of Information and Communication Technology Research 2023; 15 (1) :63-71
URL: http://ijict.itrc.ac.ir/article-1-559-en.html
The Effect of Data Augmentation Techniques on Persian Stance Detection
Mojgan Farhoodi1, Abbas Toloie Eshlaghi2, Mohamadreza Motadel3
1- Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Department of Information Technology Management, Science and Research Branch, Islamic Azad University, Tehran, Iran , toloie@gmail.com
3- Central Tehran Branch, Islamic Azad University, Tehran, Iran
Abstract:   (241 Views)
The purpose of stance detection is to identify the author's stance toward a particular topic or claim. Stance detection has become a key component in applications such as fake news detection, claim validation, argument searching, and author profiling. Although significant progress has been made in stance detection in languages such as English, little attention has been paid in some other languages, including Persian.  One of the main problems of research in Persian stance detection is the shortage of appropriate datasets. In this article, to address this problem, we consider data augmentation, the artificial creation of training data, which is used to conquer the shortage of datasets. In this research, we studied several methods of data augmentation such as EDA, back-translation, and merging source dataset with similar one in English language. The experimental results indicate that combining the primary data set with the translation of another dataset with similar content in another language (for example English) result in a significant improvement in the performance of the model.
Article number: 7
Keywords: stance detection, data augmentation. fake news, dataset
Full-Text [PDF 775 kb]   (255 Downloads)    
Type of Study: Applicable | Subject: Information Technology
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