Leveraging AraBERT for COVID-19 event monitoring on Arabic Twitter

Purpose The outbreak of COVID-19 has posed a significant public health threat, prompting the need for effective prediction and tracking of the virus’s spread. This study focuses on leveraging social media data, particularly Arabic Twitter posts, to predict and monitor COVID-19-related incidents. We aim to explore how natural language processing (NLP) techniques can be applied to extract meaningful information from Arabic tweets, offering valuable insights for health scientists and policymakers. Design/methodology/approach Given the challenges associated with Arabic NLP, including its morphological richness and ambiguity, traditional word embedding models often fail to capture the context accurately. To address this issue, we propose a deep learning-based approach utilizing AraBERT, the state-of-the-art language representation model for Arabic. By fine-tuning AraBERT, we aim to extract named entities and identify key events related to COVID-19, enabling better understanding and tracking of the disease’s spread through social media. The data are sourced from Arabic-language Twitter posts, focusing on the COVID-19 pandemic, and processed using deep learning techniques. Findings Our approach demonstrates that AraBERT, when fine-tuned for COVID-19-related tweets, can effectively capture context-rich entities and events, providing a reliable framework for real-time monitoring of the pandemic. This model outperforms traditional NLP techniques, showcasing its ability to handle the complexities of the Arabic language and improve the accuracy of event extraction in the context of social media. Originality/value This research introduces a novel method for monitoring COVID-19 using Arabic social media, highlighting the potential of deep learning models, particularly AraBERT, in enhancing information extraction from complex, context-heavy languages. The study contributes valuable insights into how social media can be used as a tool for epidemics.