A ra P rop at WANLP 2022 Shared Task: Leveraging Pre-Trained Language Models for A rabic Propaganda Detection (2022)

Abstract: This paper presents the approach taken for the shared task on Propaganda Detection in Arabic at the Seventh Arabic Natural Language Processing Workshop (WANLP 2022). We participated in Sub-task 1 where the text of a tweet is provided, and the goal is to identify the different propaganda techniques used in it. This problem belongs to multi-label classification. For our solution, we approached leveraging different transformer based pre-trained language models with fine-tuning to solve this problem. We found that MARBERTv2 outperforms in terms of performance where F1-macro is 0.08175 and F1-micro is 0.61116 compared to other language models that we considered. Our method achieved rank 4 in the testing phase of the challenge.

## introduction two thirds of eu citizens say they see false news at least once per week @xcite . propaganda, misinformation, and fake news have the power to polarise public opinion, to encourage hate speech and violent extremism, and ultimately to weaken democracies. in general terms, the spread of propaganda can be harmful to a nation and can hurt the sentiments of its people in a negative way. currently, propaganda (or persuasion) techniques have been commonly used on social media to manipulate or mislead social media users. there are instances where propaganda is used to divert attention from important issues by passing on fake and irrelevant information. propaganda introduces prejudice, by hiding the other side of things, proving them wrong by introducing an element of hypocrisy rather than by logically analyzing the facts. in a similar fashion, propaganda can also hamper the critical analysis of things and stop any meaningful discussion. some of the techniques by which propaganda is spread are loaded language, name calling, repetition, exaggeration/minimiza-tion, flag waving and many others. a detailed analysis of the other forms in which propaganda is spread is given by @xcite . since there are many forms through which propaganda can be spread, its detection requires a deeper analysis of the context in which the statement is made, rather than by directly labelling the whole document as propagandistic. the goal of the shared task is to build models for identifying such techniques in the arabic social media text (specifically tweets). in the the shared task of propaganda detection in arabic at wanlp 2022 @xcite , it consists of two subtasks (optional): subtask 1: given the text of a tweet, identify the propaganda techniques used in it (multi-label classification problem). ## related work the identification of propaganda was mainly at the level of articles. @xcite created a corpus of news articles, which were divided into four categories: propaganda, trusted, hoax, or satire. articles from eight sources were included, two of which are propagandistic. in another work by @xcite , they introduced a novel task by performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. there were eighteen propaganda techniques described from a novel corpus of news articles manually annotated at the fragment level. @xcite proposed a new multi-label multimodal task for detecting propaganda techniques used in memes from a carefully annotated new corpus of 950 memes with 22 propaganda techniques in text, image, or both. in addition, a shared task for detecting persuasive techniques in text and images was introduced at semeval 2021. @xcite . ## data the data of subtask 1 consists of ids, text, and propaganda techniques as labels. an example is provided in figure 1 . in our investigation, we found only 18 out of 21 classes annotated in the list of techniques provided by the organizer given in the training data. most frequently occurring class is loaded language (32.8%), followed by name calling/labeling, no techniques, smears and appeal to fear/prejudice and rest of the classes (20.3%), can be seen in the figure 2 . for training the system, we used the same training, development, and test data as provided by the organizer and the split of the data is given in ## results and discussion we understand that our approach is only applicable to more general aspects of arabic propaganda detection. further layers must be added to the setup to capture more specific knowledge about propaganda detection in the arabic language specific to the given dataset. ## conclusion in this work, our objective is to evaluate the performance of different transformer-based language models that are being built with simple fine-tuning. in the course of doing this, we achieved rank 4 on the challenge leaderboard without explicitly adding additional processing. we understand that propaganda detection is a challenging task. our approach sets the baseline for the general aspects of arabic propaganda detection. for future work, we can apply data augmentation, cross-validation, an ensemble of models, and further fine-tuning of model architecture specific to the task.