UAIC 1860 at S em E val-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020)

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 sentence 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 multilabel multiclass model to identify applied propaganda.

## introduction propaganda is a strong component of media ideas making it easier for reputation of people with high stature and to organizations @xcite . research on detecting propaganda has focused, especially, on news articles @xcite . using a range of psychological and rhetorical techniques, propaganda intends to manipulate deliberately people's beliefs, attitudes or actions @xcite . consequently, automatic detection and classification of propaganda in news articles is a challenging work @xcite . 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. ## 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 @xcite or character n-grams features for stylistic purposes @xcite . 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 @xcite . 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 straw man @xcite ; name calling or labelling @xcite ; repletion and black-and-white fallacy, dictatorship @xcite ; exaggeration or minimization and appeal to authority @xcite ; appeal to fear/prejudice; flag-waving and bandwagon @xcite ; causal oversimplification; whataboutism @xcite ; reductio ad hitlerum (tenibaum, 2009), etc. because for semeval-2020 task 11 there were not enough instances to represent valuable information, some of these techniques had been merged (whataboutism with straw men and red herring; bandwagon with reduction ad hitlerum) and other were eliminated (obfuscation, intentional vagueness, confusion). ## dataset and methods this section contains details about both datasets built as part of semeval-2020 task 11 "detection of propaganda techniques in news articles" and the study methodology, which was the basis for solving both sub-tasks. ## conclusion this paper presents a system participating at semeval task 11. since we performed an exhaustive investigation of propaganda detection at the fragment level, our experimental results showed that linguistic features like character and word n-grams are remarkably efficient for both tasks. the overall results are satisfactory and exceeds the baseline; however, there is still room for improvement, in predicting the techniques. larger and well annotated dataset would provide more opportunities for exploring the issue of propaganda detection in news articles, in addition building a dataset sufficient in size and diversity will allow experiments with deep learning methods,