USTHB at A r AIE val’23 Shared Task: Disinformation Detection System based on Linguistic Feature Concatenation (2023)

Abstract: In this research paper, we undertake a comprehensive examination of several pivotal factors that impact the performance of Arabic Disinformation Detection in the ArAIEval’2023 shared task. Our exploration encompasses the influence of surface preprocessing, morphological preprocessing, the FastText vector model, and the weighted fusion of TF-IDF features. To carry out classification tasks, we employ the Linear Support Vector Classification (LSVC) model. In the evaluation phase, our system showcases significant results, achieving an F _1 micro score of 76.70% and 50.46% for binary and multiple classification scenarios, respectively. These accomplishments closely correspond to the average F _1 micro scores achieved by other systems submitted for the second subtask, standing at 77.96% and 64.85% for binary and multiple classification scenarios, respectively.

## introduction in recent years, the detection of disinformation in digital content has become a critical challenge at the intersection of natural language processing and information security, spurred by the growing influence of online platforms @xcite . the arabic-speaking digital landscape, in particular, has witnessed an alarming increase in susceptibility to the dissemination of false or misleading information, a phenomenon well-documented in recent research @xcite ). the ramifications of disinformation extend beyond individual deception; they cover broader societal consequences, affecting public opinion, social cohesion, and even national security. recognizing the gravity of this issue, we actively participate in the inaugural shared task organized by araieval'2023, which focuses on disinformation detection in arabic text @xcite . our engagement in this task reflects our commitment to addressing this pressing challenge. by harnessing advanced natural language processing techniques and machine learning models, we endeavor to contribute to the development of effective disinformation detection systems tailored to the nuances of the arabic language. through rigorous experimentation and evaluation, we aim to enhance our understanding of the complexities involved and offer practical solutions to safeguard the integrity of digital discourse and information dissemination in the arabic-speaking world. to combat the proliferation of disinformation in arabic text, a growing number of research has been dedicated to developing robust and effective detection systems @xcite . much like the endeavors undertaken in the field of arabic dialect identification @xcite , disinformation detection in arabic requires a nuanced understanding of the language's intricacies @xcite , as well as the ability to sift through vast amounts of textual data @xcite to identify instances of deceptive or misleading content. in this paper, we embark on an extensive exploration of disinformation detection in arabic, drawing inspiration from the methodologies and techniques employed in previous shared tasks @xcite . leveraging these insights, we aim to build upon existing research and contribute to the ongoing efforts to enhance the accuracy and effectiveness of disinformation detection systems in arabic text. our study encompasses a comprehensive analysis of various factors influencing the performance of arabic disinformation detection, including surface and morphological preprocessing techniques @xcite , feature engineering strategies @xcite , and the implementation of state-of-the-art machine learning models. through rigorous experimentation and evaluation, we seek to provide valuable insights and practical solutions that can aid in the identification and mitigation of disinformation. this paper is organized as follows: section 2 offers insights into the dataset we have employed. moving on to section 3, we introduce our proposed system, which includes details about the cleaning and preprocessing steps discussed in section 3.1. the process of feature engineering is elucidated in section 3.2. section 3.3 is dedicated to a comprehensive discussion of our findings. finally, we wrap up the paper in section 4 with a conclusive summary of our contributions and key findings. ## description of the dataset a disinformation dataset constitutes a crucial resource for studying and comprehending the multifaceted landscape of misinformation, misleading content, and fabricated information within various digital platforms. such datasets encompass a diverse array of textual, visual, and multimedia content intentionally designed to deceive, mislead, or manipulate audiences. these datasets serve as invaluable assets for researchers, data scientists, and machine learning practitioners engaged in the development of advanced algorithms and models aimed at detecting, analyzing, and combating disinformation. by analyzing patterns, linguistic cues, and contextual elements within disinformation datasets, researchers gain insights into the tactics, strategies, and evolving nature of disinformation campaigns, thereby contributing to the enhancement of society's ability to discern and mitigate the harmful impacts of deceptive content in an increasingly interconnected information landscape. additional information regarding this dataset can be found in ## conclusion in conclusion, our comprehensive analysis of key factors in arabic disinformation detection has shed light on critical aspects that significantly influence performance. through a meticulous exploration of surface preprocessing, morphological preprocessing, the fasttext vector model, and the weighted fusion of tf-idf features, we have gained valuable insights into their impact on classification tasks. our system's noteworthy achievement of an f 1 micro score of 76.70% and 50.46% for binary and multiclass classification setups, respectively, closely aligns with the performance of other systems submitted for the second subtask. this not only reaffirms the significance of surface preprocessing and weighted tf-idf feature fusion but also positions them as robust techniques in the domain of arabic disinformation detection.