%0 Conference Proceedings %T Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection %A Da San Martino, Giovanni %A Barrón-Cedeño, Alberto %A Nakov, Preslav %Y Feldman, Anna %Y Da San Martino, Giovanni %Y Barrón-Cedeño, Alberto %Y Brew, Chris %Y Leberknight, Chris %Y Nakov, Preslav %S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda %D 2019 %8 November %I Association for Computational Linguistics %C Hong Kong, China %F da-san-martino-etal-2019-findings %X 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/. %R 10.18653/v1/D19-5024 %U https://aclanthology.org/D19-5024 %U https://doi.org/10.18653/v1/D19-5024 %P 162-170