Papers with Fine-Grained Propaganda Detection

3 papers
Fine-Tuned Neural Models for Propaganda Detection at the Sentence and Fragment levels (D19-50)

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Challenge: The system was evaluated on a unified development set without distributing the gold labels.
Approach: They propose to use fine-grained propaganda detection to build models that can explain why an article is propagandistic.
Outcome: The proposed model performed on all eighteen propaganda techniques in the corpus of the shared task.
NSIT@NLP4IF-2019: Propaganda Detection from News Articles using Transfer Learning (D19-50)

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Challenge: In this paper, we describe our approach and system description for NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection.
Approach: They propose to use document Embeddings and LSTM to detect whether a sentence contains a propagandistic agenda.
Outcome: The proposed approach ranked 21st in the NLP4IF 2019 Workshop: Shared Task on Fine-Grained Propaganda Detection.
Findings of the NLP4IF-2019 Shared Task on Fine-Grained Propaganda Detection (D19-50)

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Challenge: A shared task on fine-grained propaganda detection was organized at EMNLP-IJCNLP 2019 . 12 systems submitted systems for the FLC task, 25 for the SLC task, and 14 teams submitted a system description paper .
Approach: They present a task on fine-grained propaganda detection as part of the NLP4IF workshop at EMNLP-IJCNLP 2019 . they used a corpus of news articles annotated with an inventory of propagandist techniques at the fragment level to determine the propaganda technique used in each fragment .
Outcome: The shared task on fine-grained propaganda detection was organized at the EMNLP-IJCNLP 2019 . 12 systems submitted for the FLC task, 25 for the SLC task, and 14 submitted a system description paper .

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