@inproceedings{sharara-etal-2022-arabert,
title = "{A}ra{BERT} Model for Propaganda Detection",
author = "Sharara, Mohamad and
Mohamad, Wissam and
Tawil, Ralph and
Chobok, Ralph and
Assi, Wolf and
Tannoury, Antonio",
editor = "Bouamor, Houda and
Al-Khalifa, Hend and
Darwish, Kareem and
Rambow, Owen and
Bougares, Fethi and
Abdelali, Ahmed and
Tomeh, Nadi and
Khalifa, Salam and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.61/",
doi = "10.18653/v1/2022.wanlp-1.61",
pages = "520--523",
abstract = "Nowadays, the rapid dissemination of data on digital platforms has resulted in the emergence of information pollution and data contamination, specifically mis-information, mal-information, dis-information, fake news, and various types of propaganda. These topics are now posing a serious threat to the online digital realm, posing numerous challenges to social media platforms and governments around the world. In this article, we propose a propaganda detection model based on the transformer-based model AraBERT, with the objective of using this framework to detect propagandistic content in the Arabic social media text scene, well with purpose of making online Arabic news and media consumption healthier and safer. Given the dataset, our results are relatively encouraging, indicating a huge potential for this line of approaches in Arabic online news text NLP."
}
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<abstract>Nowadays, the rapid dissemination of data on digital platforms has resulted in the emergence of information pollution and data contamination, specifically mis-information, mal-information, dis-information, fake news, and various types of propaganda. These topics are now posing a serious threat to the online digital realm, posing numerous challenges to social media platforms and governments around the world. In this article, we propose a propaganda detection model based on the transformer-based model AraBERT, with the objective of using this framework to detect propagandistic content in the Arabic social media text scene, well with purpose of making online Arabic news and media consumption healthier and safer. Given the dataset, our results are relatively encouraging, indicating a huge potential for this line of approaches in Arabic online news text NLP.</abstract>
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%0 Conference Proceedings
%T AraBERT Model for Propaganda Detection
%A Sharara, Mohamad
%A Mohamad, Wissam
%A Tawil, Ralph
%A Chobok, Ralph
%A Assi, Wolf
%A Tannoury, Antonio
%Y Bouamor, Houda
%Y Al-Khalifa, Hend
%Y Darwish, Kareem
%Y Rambow, Owen
%Y Bougares, Fethi
%Y Abdelali, Ahmed
%Y Tomeh, Nadi
%Y Khalifa, Salam
%Y Zaghouani, Wajdi
%S Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F sharara-etal-2022-arabert
%X Nowadays, the rapid dissemination of data on digital platforms has resulted in the emergence of information pollution and data contamination, specifically mis-information, mal-information, dis-information, fake news, and various types of propaganda. These topics are now posing a serious threat to the online digital realm, posing numerous challenges to social media platforms and governments around the world. In this article, we propose a propaganda detection model based on the transformer-based model AraBERT, with the objective of using this framework to detect propagandistic content in the Arabic social media text scene, well with purpose of making online Arabic news and media consumption healthier and safer. Given the dataset, our results are relatively encouraging, indicating a huge potential for this line of approaches in Arabic online news text NLP.
%R 10.18653/v1/2022.wanlp-1.61
%U https://aclanthology.org/2022.wanlp-1.61/
%U https://doi.org/10.18653/v1/2022.wanlp-1.61
%P 520-523
Markdown (Informal)
[AraBERT Model for Propaganda Detection](https://aclanthology.org/2022.wanlp-1.61/) (Sharara et al., WANLP 2022)
ACL
- Mohamad Sharara, Wissam Mohamad, Ralph Tawil, Ralph Chobok, Wolf Assi, and Antonio Tannoury. 2022. AraBERT Model for Propaganda Detection. In Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP), pages 520–523, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.