D amascus T eam at NLP 4 IF 2021: Fighting the A rabic COVID -19 Infodemic on T witter Using A ra BERT (2021)

Abstract: The objective of this work was the introduction of an effective approach based on the AraBERT language model for fighting Tweets COVID-19 Infodemic. It was arranged in the form of a two-step pipeline, where the first step involved a series of pre-processing procedures to transform Twitter jargon, including emojis and emoticons, into plain text, and the second step exploited a version of AraBERT, which was pre-trained on plain text, to fine-tune and classify the tweets with respect to their Label. The use of language models pre-trained on plain texts rather than on tweets was motivated by the necessity to address two critical issues shown by the scientific literature, namely (1) pre-trained language models are widely available in many languages, avoiding the time-consuming and resource-intensive model training directly on tweets from scratch, allowing to focus only on their fine-tuning; (2) available plain text corpora are larger than tweet-only ones, allowing for better performance.

## introduction in the past few years, various social media platforms such as twitter, facebook, instagram, etc. have become very popular since they facilitate the easy acquisition of information and provide a quick platform for information sharing @xcite . the work presented in this paper primarily focuses on twitter. twitter is a micro-blogging web service with over 330 million active twitter users per month, and has gained popularity as a major news source and information dissemination agent over the last years. twitter provides the ground information and helps in reaching out to people in need, thus it plays an important role in aiding crisis management teams as the researchers have shown @xcite . the availability of unauthentic data on social media platforms has gained massive attention among researchers and become a hot-spot for sharing misinformation @xcite . infodemic misinformation has been an important issue due to its tremendous negative impact @xcite @xcite , it has increased attention among researchers, journalists, politicians and the general public. in the context of writing style, misinformation is written or published with the intent to mislead the people and to damage the image of an agency, entity, person, either for financial or political benefits @xcite @xcite . this paper is organized as follows: section 2 describes the related work in this domain; section 3 gives our methodology in detail; section 4 discusses the evaluation of our proposed solution and finally, the last section gives the conclusion and describes future works. ## related works there are various techniques used to solve the problem of infodemic misinformation on online social media, especially in english content. this section briefly summarizes the work in this field. @xcite have focused on a quantitative report to understand the impact of misinformation on social media in the 2016 u.s. presidential general election and its effect upon u.s. voters. ## methodology in this section, we will present our methodology by explaining the different steps of building the models, we use the same architecture for building them: data set, data preprocessing, arabert system architecture, and model training. ## evaluation and results to validate the results, we used the nlp4if tweets dataset. the training and testing sets contain 90% and 10% of total samples, respectively. we split the training data set into 90% for training and 10% for validation. in this section, we will introduce the different evaluation experiments of our implemented model on the test data. in results show that our model can detect if the tweet is "harmfull to society" or "requires attention of government entities" with high accuracy (90% and 92% respectively), if the tweet "may be of interest to the general public" or "contains false information" with a very good accuracy (84% and 86% respectively), and if the tweet is "harmfull", "needs verification", or "verifiable" with fairly good accuracy (76%, 75%, and 74% respectively). in ## conclusions the objective of this work was the introduction of an effective approach based on the arabert language model for fighting tweets covid-19 infodemic. it was arranged in the form of a twostep pipeline, where the first step involved a series of pre-processing procedures to transform twitter jargon, including emojis and emoticons, into plain text, and the second step exploited a version of arabert, which was pre-trained on plain text, to fine-tune and classify the tweets with respect to their label. future work will be directed to investigate the specific contributions of each pre-processing procedure, as well as other settings associated with the tuning, so as to further characterize the language model for the purposes of covid-19 infodemic. finally, the proposed approach will also be tested and assessed with respect to other datasets, languages and social media sources, such as facebook posts, in order to further estimate its applicability and generalizability.