@inproceedings{refaee-etal-2022-arabem,
title = "{A}ra{BEM} at {WANLP} 2022 Shared Task: Propaganda Detection in {A}rabic Tweets",
author = "Refaee, Eshrag Ali and
Ahmed, Basem and
Saad, Motaz",
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-1.62",
doi = "10.18653/v1/2022.wanlp-1.62",
pages = "524--528",
abstract = "Propaganda is information or ideas that an organized group or government spreads to influence people{\'s} opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.",
}
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<abstract>Propaganda is information or ideas that an organized group or government spreads to influence peopleś opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.</abstract>
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%0 Conference Proceedings
%T AraBEM at WANLP 2022 Shared Task: Propaganda Detection in Arabic Tweets
%A Refaee, Eshrag Ali
%A Ahmed, Basem
%A Saad, Motaz
%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 refaee-etal-2022-arabem
%X Propaganda is information or ideas that an organized group or government spreads to influence peopleś opinions, especially by not giving all the facts or secretly emphasizing only one way of looking at the points. The ability to automatically detect propaganda-related linguistic signs is a challenging task that researchers in the NLP community have recently started to address. This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets. Our system utilized a pre-trained BERT model to perform multi-class binary classification. It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.
%R 10.18653/v1/2022.wanlp-1.62
%U https://aclanthology.org/2022.wanlp-1.62
%U https://doi.org/10.18653/v1/2022.wanlp-1.62
%P 524-528
Markdown (Informal)
[AraBEM at WANLP 2022 Shared Task: Propaganda Detection in Arabic Tweets](https://aclanthology.org/2022.wanlp-1.62) (Refaee et al., WANLP 2022)
ACL