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

Sazid Zaman Khan (), Jamil As-ad (), Md Khaliluzzaman (), Toni Anwar () and Rashedul Islam ()
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Sazid Zaman Khan: International Islamic University Chittagong
Jamil As-ad: International Islamic University Chittagong
Md Khaliluzzaman: International Islamic University Chittagong
Toni Anwar: Universiti Teknologi Petronas
Rashedul Islam: International Islamic University Chittagong

Journal of Computational Social Science, 2025, vol. 8, issue 4, No 8, 19 pages

Abstract: 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.

Keywords: Representation of Islam; Islamic data mining; Topic modelling; Natural language processing (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s42001-025-00415-6

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