JUSTDeep at NLP4IF 2019 Task 1: Propaganda Detection using Ensemble Deep Learning Models

Hani Al-Omari, Malak Abdullah, Ola AlTiti, Samira Shaikh


Abstract
The internet and the high use of social media have enabled the modern-day journalism to publish, share and spread news that is difficult to distinguish if it is true or fake. Defining “fake news” is not well established yet, however, it can be categorized under several labels: false, biased, or framed to mislead the readers that are characterized as propaganda. Digital content production technologies with logical fallacies and emotional language can be used as propaganda techniques to gain more readers or mislead the audience. Recently, several researchers have proposed deep learning (DL) models to address this issue. This research paper provides an ensemble deep learning model using BiLSTM, XGBoost, and BERT to detect propaganda. The proposed model has been applied on the dataset provided by the challenge NLP4IF 2019, Task 1 Sentence Level Classification (SLC) and it shows a significant performance over the baseline model.
Anthology ID:
D19-5016
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
Venues:
EMNLP | NLP4IF | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
113–118
Language:
URL:
https://www.aclweb.org/anthology/D19-5016
DOI:
10.18653/v1/D19-5016
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5016.pdf