K now T ell C onvince at A r AIE val Shared Task: Disinformation and Persuasion Detection in A rabic using Similar and Contrastive Representation Alignment (2023)

Abstract: In an era of widespread digital communication, the challenge of identifying and countering disinformation has become increasingly critical. However, compared to the solutions available in the English language, the resources and strategies for tackling this multifaceted problem in Arabic are relatively scarce. To address this issue, this paper presents our solutions to tasks in ArAIEval 2023. Task 1 focuses on detecting persuasion techniques, while Task 2 centers on disinformation detection within Arabic text. Leveraging a multi-head model architecture, fine-tuning techniques, sequential learning, and innovative activation functions, our contributions significantly enhance persuasion techniques and disinformation detection accuracy. Beyond improving performance, our work fills a critical research gap in content analysis for Arabic, empowering individuals, communities, and digital platforms to combat deceptive content effectively and preserve the credibility of information sources within the Arabic-speaking world.

## introduction in today's information age, the rapid dissemination of digital content across various platforms has revolutionized the way information is produced, shared, and consumed. this unprecedented accessibility to information has brought numerous benefits, but it has also given rise to new challenges, particularly in the realms of misinformation, propaganda, and disinformation @xcite . identifying and addressing these issues is paramount for ensuring the integrity and credibility of information sources. while the english language has garnered substantial attention in the realm of misinformation, persuasion, and disinformation detection, it is imperative that we recognize the equal, if not greater, significance of these endeavors in the arabic language. less research on these areas will leave the arabic-speaking world vulnerable to the harmful effects of deceptive content. arabic's linguistic and cultural nuances demand tailored approaches to combat these issues effectively @xcite @xcite . disinformation, encompassing hate speech, offensive content, rumors, spam, and propaganda, presents formidable challenges in the arabicspeaking world, shaped by linguistic diversity and cultural nuances @xcite . hate speech and offensive content, intensified by cultural sensitivities, demand effective detection and mitigation to avert real-world repercussions @xcite @xcite . rumors, highly contagious within tight-knit arabic communities, necessitate vigilant monitoring to counteract panic and misinformation, exploiting cultural contexts for added complexity @xcite . spam, spanning fraudulent ads and misleading claims, pervades digital spaces in all languages, underlining the need to distinguish it from credible content for online source credibility @xcite . propaganda, a pivotal element of disinformation campaigns, influences public opinion and necessitates understanding and countering within the arabic-speaking context to protect individuals and communities from manipulation by misleading narratives @xcite . addressing these multifaceted challenges requires comprehensive research efforts and robust detection models that account for linguistic and cultural intricacies, preserving the credibility of information sources, online discourse, and public opinion in the diverse and dynamic arabic-speaking linguistic landscape. in order to address the problems mentioned above and to extend the previous related works @xcite @xcite , in this paper, we present a multi-faceted approach that combines innovative model architectures, fine-tuning strategies, and sequential learning techniques to effectively address subtask 1a of task 1 (persuasion or propaganda detection) and both subtasks of task 2 (disinformation detection) in araieval 2023 @xcite . our incorporation of contrastive learning, renormalization, sentence embedding, cosine similarity checks, and gelu activation functions within the arabic bert framework demonstrates a comprehensive strategy for detecting disinformation subtleties, including hate speech, offensive content, rumors, spam, and propaganda. our contributions not only enhance disinformation detection accuracy but also bridge the research gap in content analysis for the arabic language. task 2: this task centers on disinformation detection with two subtasks: subtask 2a for binary classification to identify disinformation in tweets and subtask 2b for multi-class classification, categorizing tweets into hate speech, offensive content, rumors, or spam categories. task 2: similar to task 1, this task also contains tweets annotated for binary and multiclass labels for subtask 2a and subtask 2b, respectively. ## system descriptions our system is an ensemble of four models, as shown in figure 1 . below, we explain every component in detail. model a -supervised contrastive learning with arabic bert: in model a, we employ contrastive learning to enhance arabic text representations. the motivation is to empower the model for binary classification tasks by improving its ability to distinguish between positive and negative examples in arabic text @xcite . contrastive learning encourages the model to capture semantic relationships effectively, benefiting applications like sentiment analysis. we fine-tune bert arabic base @xcite with a contrastive loss function, pushing the model to generate embeddings emphasizing semantic similarity and dissimilarity. during training, it promotes similar representations for similar sentences and different representations for dissimilar sentences, enhancing the model's semantic understanding. model b -sequential learning with ara-bicbert: model b adopts a sequential learning approach, fine-tuning arabicbert on taskspecific data to adapt to various arabic nlp tasks. the motivation is to enable the model to comprehend sequential relationships and context in textual data, which is crucial for tasks like text generation and named entity recognition. rationally, sequential learning involves taking the pretrained bert model and fine-tuning it on specific tasks, transferring knowledge from its general language understanding capabilities to task-specific nuances. we adjust learning rates and batch sizes for different tasks and employ task-specific loss functions during fine-tuning. model c -fine-tuned arabic bert with renormalized xnli data and sequential learning: this process entails taking the pretrained arabic bert model and fine-tuning it on the renormalized xnli dataset. during this phase, the labels in the dataset are adjusted to reflect the sentiment perspective, where neutral and entailment labels are unified into one class, and contradiction remains as a separate class. subsequently, applying sequential learning further enhances the model's adaptation to the task-specific nuances @xcite al., bert arabic base bert arabic base bert arabic base bert arabic base contrastive learning (contrastive loss) sequential learning (c.e. loss) finetuning with renormalized xnli similarity with positive sentence similarity with negative sentence cosine values gelu activation sentence embedding sequential learning (c.e. loss) cosine values ensemble of models model a model b model d model c figure 1: overall framework of our proposed methodology. ## results and discussion this section discusses the results of our runs. apart from the above mentioned, we also tested to arsas bert foot_0 . we perform a detailed ablation of what factors contribute to the better performance of the system. ## conclusion in summary, our study emphasizes the power of ensemble models and collaborative approaches in improving the accuracy of persuasion and disinformation detection tasks in arabic text. we consistently observed enhanced performance through rigorous experimentation, as evidenced by rising micro and macro f1-scores across various model combinations. these results underscore the importance of adaptability and synergy in addressing the nuanced challenges of natural language understanding tasks. whether it is fine-tuning sentiment semantics, leveraging sentence embeddings, or combining all components, ensemble models consistently outperform individual approaches. these findings offer valuable insights for arabic text processing and as a model for tackling similar challenges across languages and domains. in an ever-evolving landscape of language processing, our study highlights the significance of diverse techniques and collaborative strategies to effectively meet the complexity of natural language understanding tasks. ultimately, our research contributes to more accurate solutions and a deeper understanding of persuasive and deceptive language in the digital age.