DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1732–1738 Barcelona, Spain (Online), December 12, 2020. 1732 DUTH at SemEval-2020 Task 11: BERT with Entity Mapping for Propaganda Classification Anastasios Bairaktaris Symeon Symeonidis Database and Information Retrieval research unit, Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi 67100, Greece. {anasbair1,ssymeoni,avi}@ee.duth.gr Avi Arampatzis Abstract This report describes the methods employed by the Democritus University of Thrace (DUTH) team for participating in SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles. Our team dealt with Subtask 2: Technique Classification. We used shallow Natural Language Processing (NLP) preprocessing techniques to reduce the noise in the dataset, feature selection methods, and common supervised machine learning algorithms. Our final model is based on using the BERT system with entity mapping. To improve our model’s accuracy, we mapped certain words into five distinct categories by employing word-classes and entity recognition. 1 Introduction According to the Institute for Propaganda Analysis1, propaganda is an expression of an opinion or an action by individuals or groups deliberately designed to influence the opinions or the actions of other individuals or groups concerning predetermined ends. With the rapid change that the world wide web has made, it is evident that the means available for propaganda to be spread are more than ever before. The fact that, nowadays, news outlets can reach out to millions of people through their websites or social media demonstrates how easy it is to manipulate people with propaganda techniques or fake news. For example, political forecasts severely underperformed in predicting the results of the 2016 US presidential election and the United Kingdom European Union membership referendum (Brexit) as opposed to the consensus in social media, which is indicative of the new challenges that are upon us (Hall et al., 2018). The SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles aims to produce models that can identify text fragments with various propaganda techniques. The first subtask is a binary sequence tagging task in which a model has to return the spans that contain at least one propaganda technique. The second subtask is a multi-class classification task in which given a text fragment and the article it occurs in, participants must classify the fragment into one of 14 different propaganda classes. More details on the Task can be found on the Task Description paper (Da San Martino et al., 2020). The rest of this paper is structured as follows. Section 2 outlines some previous studies of propaganda identification. Section 3 describes our approach, while Sections 4 and 5 present experiments and results respectively. Conclusions are summarized in Section 6. 2 Background Barr´on-Cede˜no et al. (2019) presented Proppy, a publicly available real-time propaganda detection system that is used for online news. The system used four modules that include article retrieval, event identification, deduplication, and propaganda index computation. To organize the news based on their propagandistic content, they showed that when identifying propaganda, approaches that use word n-grams are less effective than those that …

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