Crowdsourced Information Authentication: A Graph-based Model from the Science of Hadith

Ayoub Ghriss

Submission Track: Track 2: ML Research Addressing Challenges Faced by Muslim Communities (Open to all)

Keywords: Truth discovery; hadith; fake news, graph learning

TL;DR: machine learning algorithm for crowdsourcing information verification inspired by hadith sciences

Abstract:

The authentication of prophetic traditions in Islam (Hadiths) is a cornerstone of Islamic jurisprudence, relying on meticulous examination of the chain of narrators and the transmitted content. This paper proposes a graph-based computational framework for the authentication of transmitted information, inspired by the principles of Hadith sciences. We jointly learn the authenticity score for each transmission and the reliability score for each narrator. The method explicitly accounts for the structure of transmission chains, narrator reliability metrics, content consistency, and the crucial aspect of corroboration (a form of collective verification) through multiple independent transmission paths, including a mechanism to discount overlapping paths. We explore several potential formulations and propose an iterative co-update algorithm with its convergence analysis. This work aims to exploit principles from over a millennium of extensive accumulated knowledge in the science of Hadith to inform the advancement of modern machine learning techniques for information verification.
Our formulation offers potential applications in digital information trustworthiness assessment and tools for Hadith scholars to leverage computational methods in analyzing the extensive Hadith corpora and its authentic collections.

Submission Number: 24