A Qualitative and Quantitative Method for Studying Religious Virtual Communities: The Case of the Salafi United Kingdom’s Community on Twitter (X)

This open-source-based article presents an automated method for identifying and tracing popular Salafi discussions online. The novelty of this method lies in its inter-disciplinary approach developed through collaboration among experts in the fields of the Middle East, Islamic studies, and computer science. The computerized model presented here harnesses machine learning techniques to accurately identify popular Salafi writings on social media and to distinguish them from the writings of Muslims from other denominations. Creating an AI-supported model to distinguish between writings on social media that pertain to two different Islamic denominations is a highly difficult task. Based on this machine learning model and the methodology that it implements, the study presented here identifies United Kingdom-based Twitter accounts that embody Salafi thinking (even if they do not utilize terminology that is manifestly Salafi) and, based on that identification, analyzes and characterizes the United Kingdom-based Salafi community on Twitter. Unlike other machine learning ideology-related studies that are focused on Salafi-jihadism, the present research is focused on quietist Salafism (Salafi-taqlidis) in the United Kingdom. The purpose of this study is to examine the virtual Salafi community in the United Kingdom, with a focus on identifying the key issues of concern to its members and assessing the influence of global Salafi trends within this UK-based community.