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A Systematic Review on Hadith Authentication and Classification Methods

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Published:23 April 2021Publication History
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Abstract

Background: A hadith refers to sayings, actions, and characteristics of the Prophet Muhammad peace be upon him. The authenticity of hadiths is crucial, because they constitute the source of legislation for Muslims with the Holy Quran. Classifying hadiths into groups is a matter of importance as well, to make them easy to search and recognize.

Objective: To report the results of a systematic review concerning hadith authentication and classification methods.

Data sources: Original articles found in ACM, IEEE Xplore, ScienceDirect, Scopus, Web of Science, Springer Link, and Wiley Online Library.

Study selection criteria: Only original articles written in English and dealing with hadith authentication and classification. Reviews, editorial, letters, grey literature, and restricted or incomplete articles are excluded.

Data extraction: Two authors were assigned to extract data using a predefined data extraction form to answer research questions and assess studies quality.

Results: A total of 27 studies were included in this review. There are 14 studies in authentication and 13 studies in classification. Most of the selected studies (17 of 27) were published in conferences, while the others (10 of 27) were published in scientific journals. Research in the area of hadith authentication and classification has received more attention in recent years (2016–2019).

Conclusions: Hadith authentication methods are classified into machine learning, rule-based, and a hybrid of rule-based and machine learning and rule-based and statistical methods. Hadith classification methods are classified into machine learning and rule-based. All classification studies used Matn, while the majority of authentication studies used isnad. As a dataset source, Sahih Al-Bukhari was used by most studies. None of the used datasets is publicly available as a benchmark dataset, either in hadith authentication or classification. Recall and Precision are the most frequent evaluation metrics used by the selected studies.

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        • Published in

          cover image ACM Transactions on Asian and Low-Resource Language Information Processing
          ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 2
          March 2021
          313 pages
          ISSN:2375-4699
          EISSN:2375-4702
          DOI:10.1145/3454116
          Issue’s Table of Contents

          Copyright © 2021 Association for Computing Machinery.

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 23 April 2021
          • Accepted: 1 November 2020
          • Revised: 1 August 2020
          • Received: 1 February 2020
          Published in tallip Volume 20, Issue 2

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