YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1509–1515 Barcelona, Spain (Online), December 12, 2020. 1509 YNU-HPCC at SemEval-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles Jiaxu Dao, Jin Wang and Xuejie Zhang School of Information Science and Engineering Kunming, China Yunnan University Contact:{wangjin, xjzhang}@ynu.edu.cn Abstract This paper summarizes our studies on propaganda detection techniques for news articles in the SemEval-2020 task 11. This task is divided into the SI and TC subtasks. We implemented the GloVe word representation, the BERT pretraining model, and the LSTM model architecture to accomplish this task. Our approach achieved good results for both the SI and TC subtasks. The macro-F1-score for the SI subtask is 0.406, and the micro-F1-score for the TC subtask is 0.505. Our method significantly outperforms the officially released baseline method, and the SI and TC subtasks rank 17th and 22nd, respectively, for the test set. This paper also compares the performances of different deep learning model architectures, such as the Bi-LSTM, LSTM, BERT, and XGBoost models, on the detection of news promotion techniques. The code of this paper is availabled at: https://github.com/daojiaxu/semeval_11. 1 Introduction Propaganda techniques need to attach importance to arouse the emotions of the receivers, sometimes even by temporarily bypassing the intellectual defenses of the receivers Pearlin and Rosenberg (1952) . Propaganda uses psychological and rhetorical techniques to achieve its purpose. Such techniques include using logical fallacies and appealing to the emotions of the audience. Logical fallacies are usually hard to spot since the argumentation, at first, might appear correct and objective (Martino et al., 2019) . However, careful analysis shows that the conclusion cannot be drawn from the premise without misusing logical rules. Another set of techniques uses emotional language to induce the audience to agree with the speaker only based on the emotional bond that is being created, provoking the suspension of any rational analysis of the argumentation. The traditional NLP task generally classifies and detects propaganda techniques at the article level, which often fails to meet more detailed requirements. This fact has also been confirmed by previous iterations of the SemEval competition, where leading solutions used convolutional neural networks (CNN), long short-term memory (LSTM) (Baziotis et al., 2018) and transfer learning techniques (Duppada et al., 2018) . The main features of an article are extracted by using the feature capture and pooling of the CNN model, but these methods can only be used at the article level and are coarse-grained detection methods. However, limited research has focused on text classification (Lewis, 1995; Song et al., 2010). News articles have also been classified using the Bi-LSTM-CNN model (Li et al., 2018) . However, there are often many propaganda techniques in one article, and most of these techniques are efficient for propaganda classification but lack the ability to detect categories of propaganda techniques. Thus, they cannot achieve good results and are less efficient in practice. Now the difficulty is to detect propaganda techniques at the fine-grained level. The SemEval-2020 Task …

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