Integrating Argumentation Features for Enhanced Propaganda Detection in A rabic Narratives on the Israeli War on G aza (2025)

Abstract: Propaganda significantly shapes public opinion, especially in conflict-driven contexts like the Israeli-Palestinian conflict. This study explores the integration of argumentation features, such as claims, premises, and major claims, into machine learning models to enhance the detection of propaganda techniques in Arabic media. By leveraging datasets annotated with fine-grained propaganda techniques and employing crosslingual and multilingual NLP methods, along with GPT-4-based annotations, we demonstrate consistent performance improvements. A qualitative analysis of Arabic media narratives on the Israeli war on Gaza further reveals the model’s capability to identify diverse rhetorical strategies, offering insights into the dynamics of propaganda. These findings emphasize the potential of combining NLP with argumentation features to foster transparency and informed discourse in politically charged settings.

## introduction propaganda is a form of communication aimed at influencing attitudes and behaviors by presenting onesided or misleading information. it often relies on emotional appeals rather than rational argumen tation to manipulate public perception and advance specific agendas or ideologies. in the digital era, the rise of social media has amplified the spread of propaganda, enabling its rapid dissemination to global audiences with little oversight. this has heightened its potential impact, as seen in events like the 2016 u.s. presidential election @xcite and during the covid19 pandemic @xcite , where social media platforms were used to polarize opinions and undermine trust in democratic institu tions. the detection of propaganda is especially critical in conflictdriven contexts, such as the narratives surrounding the israeli war on gaza. these narra tives often employ polarizing rhetoric, emotionally charged language, and manipulative techniques to shape public opinion and justify political or military actions. arabic media, both traditional and digital, plays a central role in constructing these narratives, given the geopolitical significance of the arabic speaking world. in such contexts, propaganda can be a powerful tool for inciting violence, manipulat ing perceptions, and influencing international dis course. however, detecting propaganda in arabic poses unique challenges due to the language's rich morphology, diverse dialects, and limited annotated datasets. natural language processing (nlp) offers a promising avenue for automating propaganda detec tion by analyzing linguistic patterns and rhetorical cues . while significant progress has been made in highresource languages like english, relatively little research has focused on arabic. this dispar ity highlights the need for approaches tailored to arabic's linguistic and cultural characteristics. a promising direction for enhancing propaganda detection is the integration of argumentation fea tures, such as claims and premises. propaganda and argumentation often share a structural foundation: both involve presenting claims supported by rea soning. however, propaganda diverges by infusing these structures with emotionally charged content designed to manipulate public sentiment @xcite . by identifying argumentation components within texts, it becomes possible to an alyze how propaganda leverages these structures to influence audiences, distinguishing between logical persuasion and manipulative communication. in this work, we aim to improve arabic propa ganda detection by integrating argumentation fea tures into nlp models. we then apply the enhanced models to analyze narratives from arabic media covering the israeli war on gaza. the code used in this study is available at our github repository. 1 2 related work ## baseline for propaganda detection in this study, we use the bestperforming system from the araieval 2024 shared task on propaganda detection in arabic texts by @xcite as our baseline. this system, built on arabicbert @xcite , achieved an f1 score of 0.2995 by finetuning for detecting propagandistic spans and classifying them into 23 techniques. key fea tures include the bio tagging scheme for accurate span identification and data augmentation to ad dress class imbalance for less frequent techniques. while this system was not originally developed as a baseline, we adopt it in this role for our study. its performance in the shared task makes it an ideal reference point for evaluating the improvements introduced by our approach. ## proposed methodology this study investigates the enhancement of propa ganda detection models by integrating argumenta tion features. argumentation features, such as ma jor claim, claim, and premise, provide a structured representation of the persuasive elements within a text. by leveraging the overlap between argumenta tion and propaganda, we aim to enrich the model's understanding of the underlying rhetorical strate gies. ## argumentation annotation methodology to generate argumentation annotations, we em ployed two primary approaches: ## propaganda detection evaluation and discussion the effectiveness of incorporating argumentation features into the propaganda detection task was evaluated using various approaches, including crosslingual, multilingual, and gpt4based anno tation methods. ## qualitative analysis on the media narratives on the israeli war on gaza to assess the performance of the proposed model in detecting propaganda techniques, we conducted a qualitative analysis on the fignews subset (sec tion 3.3). these examples were selected to be an notated as propagandistic and to represent both nar ratives biased against palestine and those biased against israel. all annotated examples are in ap pendix c. the analysis of the examples reveals diverse strengths and shortcomings in the model's iden tification of propaganda techniques. several ex amples showcase the model's ability to detect and label effectively, while others highlight areas for improvement in span detection and labeling accu racy. the model performed strongly in identifying a variety of propaganda techniques, particularly in cases involving appeal to fear, appeal to hypocrisy, and loaded language. for instance, in example 13: " ‫ح‬ ‫ذ‬ َّ ‫ﺭ‬ ‫ن‬ ‫ا‬ ‫ﺇ‬ ‫س‬ ‫ر‬ ‫ﺍ‬ ‫ئ‬ ‫ي‬ ‫ل‬ ‫م‬ ‫ن‬ ‫ع‬ ‫و‬ ‫ﺍ‬ ‫ق‬ ‫ب‬ ‫م‬ ‫ال‬ ‫ح‬ ‫ق‬ ‫ة‬ ‫م‬ ‫س‬ ‫ؤ‬ ‫ﻭ‬ ‫ل‬ ‫ي‬ ‫ن‬ ‫م‬ ‫ن‬ ‫ح‬ ‫م‬ ‫ا‬ ‫ﺱ‬ ‫خ‬ ‫ا‬ ‫ﺭ‬ ‫ﺝ‬ ‫ف‬ ‫ل‬ ‫س‬ ‫ط‬ ‫ي‬ ‫ن‬ " (we warned israel about the consequences of pur suing hamas officials outside palestine) was ac curately labeled as appeal to fear, as the phrase evokes concern about potential repercussions. sim ilarly, for appeal to hypocrisy, example 7 includes: " ‫ف‬ ‫ي‬ ‫ﺍ‬ ‫ل‬ ‫و‬ ‫ق‬ ‫ت‬ ‫ﺍ‬ ‫ل‬ ‫ذ‬ ‫ﻱ‬ ‫ت‬ ‫ح‬ ‫ا‬ ‫ﺭ‬ ‫ﺏ‬ ‫ف‬ ‫ي‬ ‫ه‬ ‫ﺇ‬ ‫س‬ ‫ر‬ ‫ﺍ‬ ‫ئ‬ ‫ي‬ ‫ل‬ ‫ﺇ‬ ‫ب‬ ‫ا‬ ‫ﺩ‬ ‫ﺓ‬ ‫ش‬ ‫ع‬ ‫ب‬ ‫ت‬ ُ ‫ت‬ ‫ه‬ ‫م‬ ‫ه‬ ‫ي‬ ‫ب‬ ‫إ‬ ‫ب‬ ‫ا‬ ‫ﺩ‬ ‫ﺓ‬ ‫ش‬ ‫ع‬ ‫ب‬ " (while israel is fighting genocide, it is accused of genocide), which effectively exposes perceived inconsistencies in criticism. another strong exam ple of appeal to hypocrisy appears in example 8: " ‫ﺃ‬ ‫ي‬ ‫ن‬ ‫ك‬ ‫ر‬ ‫ﺍ‬ ‫م‬ ‫ة‬ ‫ﺍ‬ ‫إل‬ ‫ن‬ ‫س‬ ‫ا‬ ‫ﻥ‬ ، ‫ﺃ‬ ‫ي‬ ‫ن‬ ‫ح‬ ‫ق‬ ‫و‬ ‫ﻕ‬ ‫ﺍ‬ ‫إل‬ ‫ن‬ ‫س‬ ‫ا‬ ‫ﻥ‬ ، ‫ﺃ‬ ‫ي‬ ‫ن‬ ‫ﺍ‬ ‫ح‬ ‫ت‬ ‫ر‬ ‫ﺍ‬ ‫م‬ ‫ه‬ ‫ف‬ ‫ا‬ ‫ل‬ ‫ج‬ ‫و‬ ‫ﺍ‬ ‫ﺏ‬ ‫ه‬ ‫ي‬ ‫ﺇ‬ ‫س‬ ‫ر‬ ‫ﺍ‬ ‫ئ‬ ‫ي‬ ‫ل‬ " the model also demonstrated proficiency in rec ognizing appeal to time, as seen in example 6 with in summary, the model demonstrates strong per formance in recognizing clear techniques such as loaded language, name calling, and appeal to fear, but occasionally mislabels neutral phrases or includes extraneous content in spans. these find ings underscore the importance of refining span selection and improving the accuracy of labels to handle nuanced cases effectively. ## conclusion this work highlights the effectiveness of integrat ing argumentation features into propaganda detec tion models for arabic texts. by combining claims, premises, and other argumentative elements with advanced nlp methodologies, we demonstrate con sistent improvements over baseline models. our analysis of arabic media narratives reveals the model's ability to detect diverse propaganda tech niques, offering valuable insights into rhetorical strategies in politically sensitive contexts. while translation and annotation quality present challenges, the findings underscore the potential of this approach for fostering transparency in conflict driven discourse. future research should focus on refining annotation and translation methods. these advancements will contribute to building robust nlp tools capable of analyzing and mitigating the impact of propaganda in sensitive geopolitical con texts.