Topic modeling as a tool for analyzing tweets: A case study of the Russia Ukraine war on Arabic social media

Farah Alshanik (), Jumana Khrais (), Rasha Obeidat (), Lamees Rababa () and Saif Aljunidi ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 9, 152-163

Abstract: The rapid emergence of social media platforms, particularly Twitter, has drastically transformed the landscape of information dissemination and public discourse, particularly during periods of conflict and war. This paradigm shift has generated abundant data that presents a valuable opportunity for analysis through text mining and natural language processing techniques. In our study, we leverage topic modeling techniques to extract and explore prevalent discussion topics surrounding the Russian-Ukraine conflict within the Middle East region. To facilitate our analysis, we carefully collect a dataset consisting of dialectical Arabic tweets specifically containing terms associated with the conflict between Ukraine and Russia. To uncover the dominant topics within the discourse, we employ a comparative analysis of two prominent topic modeling algorithms: BERTopic and Latent Dirichlet Allocation (LDA). This allows us to explore and comprehend the prevailing themes and perspectives of the discussions. The findings from our study illuminate the crucial role that social media plays in shaping public perceptions, disseminating information, and fostering discussions regarding the Russian-Ukraine war within the Middle East region. By utilizing topic modeling techniques, we are able to capture the diverse range of perspectives and themes that emerge from conversations on social media platforms. This comprehensive understanding enhances our comprehension of the intricate complexities surrounding this geopolitical conflict, thereby contributing to a deeper insight into its multifaceted nature.

Keywords: BERTopic; LDA; Public discussion; Social media; Twitter; Topic modeling; Ukraine-Russia conflict. (search for similar items in EconPapers)
Date: 2025
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