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Profiling and clustering the global market for hijabistas: a Twitter text analytics approach

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Abstract

Consumer-generated data provides a massive amount of market data that helps improve brands' decision-making processes within a highly demanding marketplace. This paper aims to investigate the dynamics behind Twitter user-generated content in relation to hijab/modest fashion based on a random sample of 144,800 tweets. Sentiment analysis was conducted, while a detection algorithm was implemented to identify the main influencers in relation to the hijab/modest fashion market. Results identify and profile the influencers and opinion leaders in the hijab/modest fashion global market. Results also show a high diversity of emojis usage in hijab-related tweets which highlighted the advantage of using them within hijab fashion brands’ communications. Finally, a partitioning around medoids (PAM) clustering method was applied to define consumer clusters. The clustering algorithm used highlights the heterogeneity and diversity of the global hijab fashion market. This study advances prior literature on hijab/modest-fashion consumers, and their opinions towards hijab brands. The study also helps marketers and decision-makers to understand consumer trends in this significant and emerging market.

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Authors and Affiliations

  1. Gulf University for Science and Technology, Mubarak Al-Abdullah, Kuwait

    Mohamed M. Mostafa

  2. Helwan University, Cairo, Egypt

    Alshaimaa Bahgat Alanadoly

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  1. Mohamed M. Mostafa
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  2. Alshaimaa Bahgat Alanadoly
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Correspondence to Mohamed M. Mostafa.

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Mostafa, M.M., Alanadoly, A.B. Profiling and clustering the global market for hijabistas: a Twitter text analytics approach. Int. j. inf. tecnol. 16, 2425–2437 (2024). https://doi.org/10.1007/s41870-023-01616-w

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