Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection

Giovanni Da San Martino, Alberto Barrón-Cedeño, Preslav Nakov

Abstract
We present the shared task on Fine-Grained Propaganda Detection, which was organized as part of the NLP4IF workshop at EMNLP-IJCNLP 2019. There were two subtasks. FLC is a fragment-level task that asks for the identification of propagandist text fragments in a news article and also for the prediction of the specific propaganda technique used in each such fragment (18-way classification task). SLC is a sentence-level binary classification task asking to detect the sentences that contain propaganda. A total of 12 teams submitted systems for the FLC task, 25 teams did so for the SLC task, and 14 teams eventually submitted a system description paper. For both subtasks, most systems managed to beat the baseline by a sizable margin. The leaderboard and the data from the competition are available at http://propaganda.qcri.org/nlp4if-shared-task/.
Anthology ID:
D19-5024
Volume:
Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Anna Feldman, Giovanni Da San Martino, Alberto Barrón-Cedeño, Chris Brew, Chris Leberknight, Preslav Nakov
Venue:
NLP4IF
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
162–170
Language:
URL:
https://aclanthology.org/D19-5024
DOI:
10.18653/v1/D19-5024
Bibkey:
Cite (ACL):
Giovanni Da San Martino, Alberto Barrón-Cedeño, and Preslav Nakov. 2019. Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 162–170, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection (Da San Martino et al., NLP4IF 2019)
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https://preview.aclanthology.org/improve-issue-templates/D19-5024.pdf
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