FROM STORIES TO STRATEGY: AI-BASED INSIGHTS INTO MUSLIM REVERT NARRATIVES FOR DIGITAL DA’WAH

Abstract

This article presents an AI-assisted qualitative study of 1,192 Muslim reversion narratives sourced from online platforms. The objective is to identify recurring themes, trigger events, and motivational pathways in the reversion journey. Data was collected from YouTube channels and Islamic websites, processed through web scraping and transcript extraction tools (yt-dlp, BeautifulSoup), and structured using OpenAI GPT prompts with manual validation. Thematic coding, clustering algorithms (KMeans), and Principal Component Analysis (PCA) were employed to analyze patterns. The findings highlight Tawheed, the Qur’an, and Muslim behavior as key motivational factors, while interaction with Muslims and online content served as common trigger events. Gender and age patterns were also observed, suggesting different influences across demographic groups. The study offers data-driven insights for more empathetic and targeted digital Da’wah strategies and demonstrates the potential of AI in analyzing religious narratives for academic and practical applications.