Abstract
Propaganda and the spreading of fake news through social media have become a serious problem in recent years. In this paper we present our approach for the shared task on propaganda detection in Arabic in which the goal is to identify propaganda techniques in the Arabic social media text. We propose a semantic similarity detection model to compare text in the test set with the sentences in the train set to find the most similar instances. The label of the target text is obtained from the most similar texts in the train set. The proposed model obtained the micro F1 score of 0.494 on the text data set.- Anthology ID:
- 2022.wanlp-1.57
- Volume:
- Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Editors:
- Houda Bouamor, Hend Al-Khalifa, Kareem Darwish, Owen Rambow, Fethi Bougares, Ahmed Abdelali, Nadi Tomeh, Salam Khalifa, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 501–505
- Language:
- URL:
- https://aclanthology.org/2022.wanlp-1.57
- DOI:
- 10.18653/v1/2022.wanlp-1.57
- Cite (ACL):
- Salar Mohtaj and Sebastian Möller. 2022. TUB at WANLP22 Shared Task: Using Semantic Similarity for Propaganda Detection in Arabic. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 501–505, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- TUB at WANLP22 Shared Task: Using Semantic Similarity for Propaganda Detection in Arabic (Mohtaj & Möller, WANLP 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.wanlp-1.57.pdf