NGU CNLP at WANLP 2022 Shared Task: Propaganda Detection in A rabic (2022)

Abstract: This paper presents the system developed by the NGU_CNLP team for addressing the shared task on Propaganda Detection in Arabic at WANLP 2022. The team participated in the shared tasks’ two sub-tasks which are: 1) Propaganda technique identification in text and 2) Propaganda technique span identification. In the first sub-task, the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques or to detect that no propaganda technique is being used in that piece of text. As such, this first sub-task is a multi-label classification problem with a pool of 18 possible labels. Subtask 2 extends sub-task 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. For task 1, a combination of a data augmentation strategy coupled with an enabled transformer-based model comprised our classification model. This classification model ranked first amongst the 14 systems participating in this subtask. For sub-task two, a transfer learning model was adopted. The system ranked third among the 3 different models that participated in this subtask.

## introduction the term propaganda was coined in the seventeenth century as a means of disseminating noble ideas among groups of individuals. over time, it has become known for referring to the use of infused ideas, news or partial arguments to groups of people with the intention of manipulating their beliefs and behaviors, typically, towards deceptive agendas. in today's world, we can find various forms of propaganda in almost every newspaper article, social media post or mass media broadcasting. it is rarely the case that individuals are simply informed without being pervasively biased. moreover, propaganda propagation is no longer exclusively dominated by religious, political or demographic entities, but even by individuals where a variety of agendas are involved. with such huge proliferation and the expected undesired influences, it is of utmost importance to be able to detect faulty or misleading information for the purpose of efficiently and promptly handling the widespread of fallacies. however, detecting computational propaganda is a significantly involved task especially with the ever growing efforts to make it go inconspicuous. by leveraging tools from machine learning (ml), it is possible to automate the necessary nlp tasks required to detect such malicious agendas. computational propaganda detection has gained immense attention @xcite @xcite in the nlp community. for example, google and facebook, are currently testing ml-powered fact-checking tools to investigate the authenticity of shared information for the purpose of fighting potential "infodemics" @xcite which are a form of propaganda. typically, nlp tasks utilize several modeling approaches such as n-gram models and neural networks models. in n-gram modeling, n-words are being paired and processed. neural networks are essential to the success of numerous nlp tasks. nlp models have been revolutionized by the introduction of contextualized embeddings such as the the bert transformer-based language model, first introduced by google @xcite . the idea of the bert-model is that it can read a sentence simultaneously in both directions. applying this to the task of propaganda detection is made possible through two subtasks. the first of which is the identification of the propaganda technique in the sentence together with the corresponding text span. the second subtask is concerned with the classification of the deployed propaganda technique out of the 18 well-known propaganda techniques @xcite . in @xcite , a logistic regression-based model was proposed to detect a propagandist text, along with features acquired from linguistic inquiry and word count (liwc) text analysis software, to solve a binary classification problem. fine-tuning of the bert transformer model was performed in @xcite where, prior to using the bert architecture, the authors initially concentrated on the pre-processing phases to offer additional details about the language dataset train dev dev_test no. of tweets 504 52 52 in @xcite , some linguistic characteristics and global noncontextual word embeddings were exploited. this paper presents the system developed by the ngu_cnlp team for addressing the shared task on propaganda detection in arabic at wanlp 2022 @xcite . the team participated in the shared tasks' two sub-tasks which are: 1) propaganda technique identification in text and 2) propaganda technique span identification. in the first sub-task the goal is to detect all employed propaganda techniques in some given piece of text out of a possible 17 different techniques, or to detect that no propaganda technique is being used in that piece of text. as such, this first sub task is a multi-label classification problem with a pool of 18 possible labels. subtask 2 extends subtask 1, by requiring the identification of the exact text span in which a propaganda technique was employed, making it a sequence labeling problem. ## subtask 1: propaganda classification the first sub-task our team participated in, was a multi-label classification problem. in this subtask, the input is a single piece of text (a tweet), and the required output, is the set of the propaganda techniques used in it. the evaluation metric for this subtask was the micro-average f 1 score. ## subtask 2: propaganda span detection in the second task, the goal was not only to identify propaganda techniques used in a piece of text, but the exact text span that represents each technique. this can be thought of as a sequence tagging task or as a token classification @xcite .