UAIC1860 at SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles

Proceedings of the 14th International Workshop on Semantic Evaluation, pages 1835–1840 Barcelona, Spain (Online), December 12, 2020. 1835 UAIC1860 at SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles Vlad Ermurachi1, Daniela Gîfu1,2 1Faculty of Computer Science, “Alexandru Ioan Cuza” University of Iasi 2Institute of Computer Science, Romanian Academy - Iasi Branch {vlad.ermurachi, daniela.gifu}@info.uaic.ro Abstract The “Detection of Propaganda Techniques in News Articles” task at the SemEval 2020 competition focuses on detecting and classifying propaganda, pervasive in news article. In this paper, we present a system able to evaluate on fragment level, three traditional text representation techniques for these study goals, using: tf*idf, word and character n-grams. Firstly, we built a binary classifier able to provide corresponding propaganda labels, propaganda or non-propaganda. Secondly, we build a multiclass model to identify applied propaganda. 1 Introduction Propaganda is a strong component of media ideas making it easier for reputation of people with high stature and to organizations (Thota et al., 2018; Gifu et al., 2014). Research on detecting propaganda has focused, especially, on news articles (Fitzmaurice, 2018; Gifu and Dima, 2014). Using a range of psychological and rhetorical techniques, propaganda intends to manipulate deliberately people’s beliefs, attitudes or actions (Al-Hindawi and Kamil, 2017). Consequently, automatic detection and classification of propaganda in news articles is a challenging work (Martino et al., 2019). The goal of this paper is to implement an automatic system, which imply two fragment level classifications for the presence of propaganda in news articles. First, a binary classification model, able to provide corresponding propaganda labels, propaganda or non-propaganda. Second, a multilabel multiclass model in order to identify applied propaganda. The rest of the paper is structured as follows: section 2 describes other works related to propaganda identification, section 3 presents the dataset and methodology of this study, section 4 briefly relates an analysis and the results we have obtained, followed by section 5 with the conclusions. 2 Related Work This topic has attracted significant attention in recent years, evidenced by increasing number of workshops of the same competition (e.g. Fake News Challenge Stance Detection Task 2018, SemEval- 2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection). Thus, work on this topic was never followed by high results, as this problem is highly subjective and text classification even for humans is very controversial and biased. Most of the authors used Bag of Words features, usually normalized with tf*idf (Saleh et al., 2019; Barro´n-Ceden˜o et al., 2019a) or character n-grams features for stylistic purposes (Stamatos, 2009). For this research, we focused more on the character-level features, which are capturing various style markers (e.g. prefixes, suffixes, etc.) found in recent research (Barro´n-Ceden˜o et al., 2019b). Martino and his team mention 18 most important techniques used in news articles, considered with significant values for this task: loaded language, red herring, obfuscation, intentional vagueness, confusion, and This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/. 1836 straw man (Weston, 2018); name calling or labelling (Miller, 1939); repletion and …

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