A novel classification to categorise original hadith detection techniques

• Published in: International journal of information technology (Singapore. Online) 2022, Vol.14 (5), p.2361-2375
• Main Authors: Fadele, Alaba Ayotunde, Kamsin, Amirrudin, Ahmad, Khadher, Hamid, Habiba
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computer Imaging; Computer Science; Image Processing and Computer Vision; Machine Learning; Original Research; Pattern Recognition and Graphics; Software Engineering; Vision
• ISSN: 2511-2104; 2511-2112
• DOI: 10.1007/s41870-021-00649-3

Hadith is a set of Islamic byelaws based on the teachings in the holy Quran. Arabic natural language processing (ANLP) tools possess features, which are mainly used for explaining Qur'an verses. In the last few years, research work in identifying both fake and authentic hadith has drawn a great attention. As of late, there are numerous hadith whose legitimacy is questionable. Currently, there are efforts to make the hadith available in digital form and disseminate it on the web and social media. This paper discusses fake hadith detection techniques, such as knowledge driven, hybrid and data driven. It also highlights various hadith detection mechanisms, their challenges and methods for identifying fake hadith. The study presents a novel taxonomy/classification of hadith detection techniques. Our taxonomy is unique compared to others because all hadith components are categorized based on four layers which include authority, narrators, Ma’tn and Isnad and status.