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Reading between the lines with topic models and machine learning: Islam’s representation on Wikipedia

Author

Listed:
  • Sazid Zaman Khan

    (International Islamic University Chittagong, Department of Computer Science and Engineering)

  • Jamil As-ad

    (International Islamic University Chittagong, Department of Computer Science and Engineering)

  • Md Khaliluzzaman

    (International Islamic University Chittagong, Department of Computer Science and Engineering)

  • Toni Anwar

    (Universiti Teknologi Petronas, Department of Computer and Information Sciences)

  • Rashedul Islam

    (International Islamic University Chittagong, Department of Computer Science and Engineering)

Abstract

Islam is a highly searched topic on the World Wide Web. Thousands of articles on Islam can be found on the web. While there are tons of websites, articles and blogs on the web, Wikipedia is one of the primary sources of information from which an interested reader can know about Islam. The representation of Islam on such an important information source is worthy of investigation. In this work, we first construct a representative dataset on Islam using Wikipedia articles. Afterwards, we apply several topic modelling and machine learning based approaches on the newly constructed dataset to find representation of Islam on Wikipedia. Also, we design two algorithms based on word2vec to find the inter topic similarity and intra topic similarity for the topic models. The intra topic similarity algorithm agrees well with human judgment of topic resolution and coherence of topics. As topic models find the dominant topics prevailing in a natural language document corpus, the intra topic similarity algorithm can be used as a new metric to find the coherence of single topics within the topic model.

Suggested Citation

  • Sazid Zaman Khan & Jamil As-ad & Md Khaliluzzaman & Toni Anwar & Rashedul Islam, 2025. "Reading between the lines with topic models and machine learning: Islam’s representation on Wikipedia," Journal of Computational Social Science, Springer, vol. 8(4), pages 1-19, November.
    • Handle: RePEc:spr:jcsosc:v:8:y:2025:i:4:d:10.1007_s42001-025-00415-6
    DOI: 10.1007/s42001-025-00415-6
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    References listed on IDEAS

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    1. Xiang Zheng & Jiajing Chen & Erjia Yan & Chaoqun Ni, 2023. "Gender and country biases in Wikipedia citations to scholarly publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 219-233, February.
    2. Muhammad Junaid Ghauri & Salma Umber, 2019. "Exploring the Nature of Representation of Islam and Muslims in the Australian Press," SAGE Open, , vol. 9(4), pages 21582440198, December.
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