Nullpointer at A r AIE val Shared Task: A rabic Propagandist Technique Detection with Token-to-Word Mapping in Sequence Tagging (2024)

Abstract: This paper investigates the optimization of propaganda technique detection in Arabic text, including tweets & news paragraphs, from ArAIEval shared task 1. Our approach involves fine-tuning the AraBERT v2 model with a neural network classifier for sequence tagging.Experimental results show relying on the first token of the word for technique prediction produces the best performance. In addition, incorporating genre information as a feature further enhances the model’s performance. Our system achieved a score of 25.41, placing us 4th on the leaderboard. Subsequent post-submission improvements further raised our score to 26.68.

## introduction the spread of propagandistic content on social media and news platforms poses a significant challenge to the integrity of information consumed by the public. detecting and countering such content is crucial for maintaining a well-informed society. identifying propaganda techniques in arabic texts presents unique challenges due to the language's complex morphology and the use of diverse dialects. our main contribution is developing an optimized system that leverages arabert for embeddings and a neural network classifier for sequence tagging to detect propagandistic techniques in arabic texts. we propose a robust preprocessing pipeline for arabic, addressing unicode inconsistencies, misaligned annotations, and user-mention normalization. we also evaluate and compare different sequence tagging approaches. the paper is structured as follows: section 2 describes the related work. section 3 describes the data preprocessing steps. section 4 discusses the sequence tagging prediction approaches. section 5 presents the details of our system. section 6 reports on the experimental results and discussions. section 7 concludes the paper. ## related work detecting propaganda and persuasion techniques in arabic text has gained significant attention in recent years. @xcite have introduced the araieval shared task, focusing on detecting propagandistic techniques in unimodal and multimodal arabic content, highlighting the importance of this task in the arabic language context. @xcite have provided an overview of the wanlp 2022 shared task on propaganda detection in arabic, discussing the challenges and significance of this task in the arabic language domain. several studies have explored the use of large language models for propaganda detection. @xcite investigated the application of large language models for propaganda span annotation, showcasing their potential for identifying propagandistic content. in a follow-up study, @xcite have evaluated the capability of gpt-4 in identifying propaganda and annotating propaganda spans in news articles, demonstrating the effectiveness of advanced language models in this domain. the araieval shared task, organized by @xcite , focused on persuasion techniques and disinformation detection in arabic text, emphasizing the need for language-specific approaches in tackling these issues. these studies demonstrate the growing interest in propaganda detection and the diverse approaches explored to address this challenge, particularly in arabic. our work builds upon these existing efforts, focusing on the role of token-to-word mapping in arabic sequence tagging for propaganda detection. ## preprocessing the data in this section, we describe our data and the preprocessing steps applied to ensure the annotation consistency and accuracy in the text snippets. ## sequence tagging prediction approaches this section discusses various approaches to sequence tagging for detecting propagandistic techniques in text. ## the propagandistic technique detection system in this section, we describe the system used for the task of detecting propagandistic techniques in arabic text snippets. our system is based on the arabert version 2 pre-trained model, followed by a neural network classifier for sequence tagging as shown in figure 1 . figure 1 (a) shows the structure of one of our classifiers and figure 1 (b) shows the overall structure. there is one classifier for each token in the input. of the neural network is quite simple; it has no hidden layers, and has a total of 1536 • 24 = 36, 864 parameters to train. ## conclusion the study underscores the importance of methodological choices in improving the accuracy of propaganda technique detection models. concatenating the [cls] token embedding and leveraging the word-level-first sequence tagging approach, particularly when supplemented with genre information, proves to be a robust strategy. future research should continue to explore the integration of additional contextual information and refine these techniques to further enhance the detection and understanding of propagandistic content across various languages and genres.