@inproceedings{saleh-etal-2019-team,
title = "Team {QCRI}-{MIT} at {S}em{E}val-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection",
author = "Saleh, Abdelrhman and
Baly, Ramy and
Barr{\'o}n-Cede{\~n}o, Alberto and
Da San Martino, Giovanni and
Mohtarami, Mitra and
Nakov, Preslav and
Glass, James",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2182",
doi = "10.18653/v1/S19-2182",
pages = "1041--1046",
abstract = "We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9{\%} accuracy on the manually annotated testset, and 60.8{\%} on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing.",
}
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<abstract>We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9% accuracy on the manually annotated testset, and 60.8% on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing.</abstract>
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%0 Conference Proceedings
%T Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection
%A Saleh, Abdelrhman
%A Baly, Ramy
%A Barrón-Cedeño, Alberto
%A Da San Martino, Giovanni
%A Mohtarami, Mitra
%A Nakov, Preslav
%A Glass, James
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F saleh-etal-2019-team
%X We describe our submission to SemEval-2019 Task 4 on Hyperpartisan News Detection. We rely on a variety of engineered features originally used to detect propaganda. This is based on the assumption that biased messages are propagandistic and promote a particular political cause or viewpoint. In particular, we trained a logistic regression model with features ranging from simple bag of words to vocabulary richness and text readability. Our system achieved 72.9% accuracy on the manually annotated testset, and 60.8% on the test data that was obtained with distant supervision. Additional experiments showed that significant performance gains can be achieved with better feature pre-processing.
%R 10.18653/v1/S19-2182
%U https://aclanthology.org/S19-2182
%U https://doi.org/10.18653/v1/S19-2182
%P 1041-1046
Markdown (Informal)
[Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection](https://aclanthology.org/S19-2182) (Saleh et al., SemEval 2019)
ACL
- Abdelrhman Saleh, Ramy Baly, Alberto Barrón-Cedeño, Giovanni Da San Martino, Mitra Mohtarami, Preslav Nakov, and James Glass. 2019. Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 1041–1046, Minneapolis, Minnesota, USA. Association for Computational Linguistics.