3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1716–1721 Barcelona, Spain (Online), December 12, 2020. 1716 3218IR at SemEval-2020 Task 11: Conv1D and Word Embedding in Propaganda Span Identification at News Articles Dimas Sony Dewantara Faculty of Computer Science Universitas Indonesia Depok, Indonesia Indonesian Institute of Sciences Jakarta, Indonesia dimas.sony91@ui.ac.id Indra Budi Faculty of Computer Science Universitas Indonesia Depok, Indonesia indra@cs.ui.ac.id Muhammad Okky Ibrohim Faculty of Computer Science Universitas Indonesia Depok, Indonesia okkyibrohim@cs.ui.ac.id Abstract In this paper, we present the result of our experiment with a variant of 1 Dimensional Convolutional Neural Network (Conv1D) hyper-parameters value. We describe the system entered by the team of Information Retrieval Lab. Universitas Indonesia (3218IR) in the SemEval 2020 Task 11 Sub Task 1 about propaganda span identification in news articles. The best model obtained an F1 score of 0.24 in the development set and 0.23 in the test set. We show that there is a potential for performance improvement through the use of models with appropriate hyper-parameters. Our system uses a combination of Conv1D and GloVe as Word Embedding to detect propaganda in the fragment text level. 1 Introduction News articles can have many forms of presentation. A news article often tends to influence the reader to have some particular view following the author’s agendas. The method usually used is to add some bias to the article being written (Baeza-Yates, 2018). On a different level, news management sometimes has lacking neutrality shapes information by emphasizing positive or negative aspects purposefully (Jowett and O’Donnell, 2006). Identifying bias is not easy. Often the writer is not realized that he has included his bias in his writing. In another case, the writer deliberately introduces bias into his writings, intending to influence his readers. The last condition is known as propaganda. Propaganda is a shape of opinion or action by individuals or groups deliberately designed to change the perspective or actions of other individuals or groups regarding predetermined ends (Jackall, 1995). Da San Martino et al. (Da San Martino et al., 2020) organized the SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles. This propaganda refers to whenever information is shaping purposefully to foster a predetermined agenda. This research focuses on subtask 1 about span identification that indicated the location of the text fragment of propaganda in news articles. Propaganda span identification is a problem of detecting text fragment in a sentence or paragraph containing propaganda techniques. The purpose of this task is to detect the presence of words or phrases that cause bias in the information on purpose or called propaganda. This study proposes a deep learning approach, specifically 1 Dimensional Convolutional Neural Network (Conv1D) combined with word embedding (GloVe) in detecting text fragment related to propaganda. Conv1D, as part of Convolutional Neural Network (CNN), is one of the deep learning approaches that has been widely used. Convolutional Neural Network is widely used in computer vision, especially in image classifications where data is processed in the form of 2 dimensions (Hussain et …

Content truncated to 500 words