A Data-Driven Exploration of a New Islamic Fatwas Dataset for Arabic NLP Tasks

Ohoud Alyemny (), Hend Al-Khalifa and Abdulrahman Mirza
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Ohoud Alyemny: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Hend Al-Khalifa: Department of Information Technology, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia
Abdulrahman Mirza: Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 12372, Saudi Arabia

Data, 2023, vol. 8, issue 10, 1-15

Abstract: Islamic content is a broad and diverse domain that encompasses various sources, topics, and perspectives. However, there is a lack of comprehensive and reliable datasets that can facilitate conducting studies on Islamic content. In this paper, we present fatwaset , the first public Arabic dataset of Islamic fatwas. It contains Islamic fatwas that we collected from various trusted and authenticated sources in the Islamic fatwa domain, such as agencies, religious scholars, and websites. Fatwaset is a rich resource as it does not only contain fatwas but also includes a considerable set of their surrounding metadata. It can be used for many natural language processing (NLP) tasks, such as language modeling, question answering, author attribution, topic identification, text classification, and text summarization. It can also support other domains that are related to Islamic culture, such as philosophy and language art. We describe the methodology and criteria we used to select the content, as well as the challenges and limitations we faced. Additionally, we perform an Exploratory Data Analysis (EDA), which investigates the dataset from different perspectives. The results of the EDA reveal important information that greatly benefits researchers in this area.

Keywords: fatwas; exploratory data analysis; Islamic content; natural language processing (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2023
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