YNU - HPCC at S em E val-2020 Task 11: LSTM Network for Detection of Propaganda Techniques in News Articles (2020)

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- F 1 - score for the SI subtask is 0.406, and the micro- F 1 - 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.

## 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 @xcite . 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 @xcite . 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) @xcite and transfer learning techniques @xcite . 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 @xcite . news articles have also been classified using the bi-lstm-cnn model @xcite . 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 11, "detection of propaganda techniques in news articles", is designed to promote research on this task. we used the word embedded representation of the pretrained model and lstm model to detect the news article propaganda techniques at a fine-grained level, and we also evaluate the performance among different neural network models on this task. the task consists of two subtasks. 1. span identification (si): given a plain-text document, identify those specific fragments that contain at least one propaganda technique @xcite . this is a binary sequence tagging task. we need to detect which fragments of the news article belong to the propaganda technique and mark the fragments with begin offset and end offset. the span ranges from begin offset to end offset-1. the rest of the paper is organized as follows. section 2 describes the details of the lstm used in our system. section 3 presents the experimental results. conclusions and future works are described in section 4. ## system description we implemented lstm model to accomplish this task. meanwhile, the representations of input words are trained by using glove model @xcite . ## experimental results in section 3, we first introduce the dataset of this task, and then we analyze the performance of different neural networks and integrated learning models for this task. ## conclusion in this paper, we presented our system for the semeval-2020 task 11, which leverages lstm and pretrained word embeddings without using human-engineered features for representation learning. our experimental results show that the lstm model with glove word embeddings can get better performance according to the scores of different neural network models and integration models on this task. the main goal of this task is to detect propaganda techniques in news articles at a fine-grained level, not just to make coarse judgments about whether the news articles use propaganda techniques. @xcite it is known that neural networks perform well on large training sets, but sometimes a large, accurately labeled dataset cannot be obtained. for future work, the development of propaganda technology detection in news articles can be greatly improved in the pretraining model and the integrated model architecture.