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.

中文翻译：

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圣训认证和分类方法的系统评价

背景： 圣训指的是先知穆罕默德的言论、行动和特征，愿他平安。圣训的真实性至关重要，因为它们构成了穆斯林对《古兰经》的立法来源。将圣训分组也很重要，以使它们易于搜索和识别。客观的：报告有关圣训认证和分类方法的系统审查结果。数据源：在 ACM、IEEE Xplore、ScienceDirect、Scopus、Web of Science、Springer Link 和 Wiley 在线图书馆中找到的原创文章。研究选择标准: 只有英文原创文章，涉及圣训认证和分类。评论、社论、信件、灰色文献以及受限或不完整的文章被排除在外。数据提取：两名作者被分配使用预定义的数据提取表格来提取数据，以回答研究问题并评估研究质量。结果: 本次审查共纳入 27 项研究。有 14 项认证研究和 13 项分类研究。大多数选定的研究（27 篇中的 17 篇）发表在会议上，而其他研究（27 篇中的 10 篇）发表在科学期刊上。近年来（2016-2019）圣训认证和分类领域的研究受到更多关注。结论: 圣训认证方法分为机器学习、基于规则、基于规则和机器学习以及基于规则和统计的混合方法。圣训分类方法分为机器学习和基于规则。所有分类研究都使用 Matn，而大多数身份验证研究使用 isad。作为数据集来源，大多数研究都使用 Sahih Al-Bukhari。无论是在圣训认证还是分类中，所使用的数据集都不能作为基准数据集公开获得。召回率和精确率是所选研究最常用的评估指标。