Fabricated Hadith Detection: A Novel Matn-Based Approach With Transformer Language Models
Kamel Gaanoun, Mohammed Alsuhaibani
Abstract
Muslims rely primarily on the Quran and the Hadiths in all their spiritual life and consider them as sacred sources. If the Quran is God’s word, then the Hadiths are God’s instructions in the words of the Prophet Muhammad. Since Hadiths are transmitted through multiple narrators, they have been extensively studied to ensure their authenticity. The purpose of this study is to detect fabricated Hadiths, or Mawdu, which is the type of Hadith most rejected by Muslim scholars. The study utilises the central text and content of Hadith, Matn, rather than solely focusing on Hadith chain of narrators, Sanad. In order to accomplish this, we create and release the first dataset dedicated to Mawdu Hadiths, called MAHADDAT. Furthermore, we set up a Mawdu Hadith (MH) detection system based on a transformer language model, BERT, achieving a 92.47% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{F}1_{MH}$ </tex-math></inline-formula> score.