This study examines the dynamics of digital Islamophobia across major social media platforms in Europe, focusing on how disinformation, algorithmic amplification, and networked interactions shape the visibility of anti-Muslim narratives. Drawing on a dataset of 2.8 million publicly available posts collected over a 12-month period (January–December 2023) from X, Facebook, YouTube, and Reddit, the research analyzes content originating from five European countries: the United Kingdom, France, Germany, Italy, and the Netherlands. A mixed-methods approach combining supervised machine learning (Support Vector Machines, 87% accuracy), sentiment analysis, and network centrality measures is employed to identify patterns of hate speech, emotional engagement, and content diffusion. The findings indicate that a relatively small cluster of highly connected accounts drives a disproportionate share of Islamophobic content, while emotionally charged narratives, particularly those invoking fear and anger, significantly increase engagement levels. Temporal analysis further suggests that spikes in Islamophobic discourse are often associated with major political and geopolitical events. While acknowledging limitations in geolocation accuracy and platform coverage, the study demonstrates how structurally embedded platform dynamics contribute to the normalization and persistence of exclusionary narratives. The article contributes to debates on digital governance, social cohesion, and the broader implications of online hate for democratic discourse in Europe.
Muhammad Asad Latif (2026) studied this question.