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Linguistic features evaluation for hadith authenticity through automatic machine learning
Mohamed, Emad Sarwar, Raheem
Mohamed, Emad
Sarwar, Raheem
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2021-11-13
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Abstract
There has not been any research that provides an evaluation of the linguistic features extracted from the matn (text) of a Hadith. Moreover, none of the fairly large corpora are publicly available as a benchmark corpus for Hadith authenticity, and there is a need to build a “gold standard” corpus for good practices in Hadith authentication. We write a scraper in Python programming language and collect a corpus of 3651 authentic prophetic traditions and 3593 fake ones. We process the corpora with morphological segmentation and perform extensive experimental studies using a variety of machine learning algorithms, mainly through Automatic Machine Learning, to distinguish between these two categories. With a feature set including words, morphological segments, characters, top N words, top N segments, function words and several vocabulary richness features, we analyse the results in terms of both prediction and interpretability to explain which features are more characteristic of each class. Many experiments have produced good results and the highest accuracy (i.e., 78.28%) is achieved using word n-grams as features using the Multinomial Naive Bayes classifier. Our extensive experimental studies conclude that, at least for Digital Humanities, feature engineering may still be desirable due to the high interpretability of the features. The corpus and software (scripts) will be made publicly available to other researchers in an effort to promote progress and replicability.
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Mohamed, E. and Sarwar, R. (2022) Linguistic features evaluation for hadith authenticity through automatic machine learning. Digital Scholarship in the Humanities, 37(3), pp.830-843, https://doi.org/10.1093/llc/fqab092
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en
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This is an accepted manuscript of an article published by OUP in Digital Scholarship in the Humanities on 13/11/2021.
The accepted version of the publication may differ from the final published version.
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2055-7671