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

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.

LeCun at SemEval-2021 Task 6: Detecting Persuasion Techniques in Text Using Ensembled Pretrained Transformers and Data Augmentation

“…There have been efforts in persuasion techniques identification and classification using machine and deep learning-based approaches. The authors in (Al-Omari et al, 2019) used word embeddings with BERT (Devlin et al, 2019) and BiLSTM (Schuster * Equal Contribution and Paliwal, 1997) for binary detection of propaganda spans. Authors in (Altiti et al, 2020) experimented with a CNN (LeCun et al, 1999), BiLSTM and BERT and showed BERT to have the best accuracy on classifying persuasion techniques in propaganda spans.…”

FPAI at SemEval-2021 Task 6: BERT-MRC for Propaganda Techniques Detection

“…There was also a related previous task on finegrained propaganda detection (Da San Martino et al, 2019), where the participants used Transformer-style models, LSTMs and ensembles (Fadel et al,2019;Hou and Chen,2019;Hua,2019). Some approaches further used non-contextualized word embeddings, e.g., based on FastText and GloVe (Gupta et al,2019;Al-Omari et al, 2019), or handcrafted features such as LIWC, quotes and questions (Alhindi et al, 2019). Moreover, Martino et al2020 analysed computational propaganda detection from Text Perspective and Network Perspective, argued for the need of combined efforts blending Natural Language Processing, Network Analysis, and Machine Learning.…”

Hitachi at SemEval-2020 Task 11: An Empirical Study of Pre-Trained Transformer Family for Propaganda Detection

“…Some recent studies (Yoosuf and Yang, 2019;Vlad et al, 2019;Hua, 2019;Fadel et al, 2019;Tayyar Madabushi et al, 2019) utilized PLMs such as BERT (Devlin et al, 2019) to detect fine-grained propaganda. Our work is related to the studies of (Fadel et al, 2019;Al-Omari et al, 2019), which employed ensemble models with PLMs. Different from these studies, we further investigate the number of PLMs.…”