Walid Magdy

The 2022 FIFA World Cup in Qatar was as much a political event as a sporting one, marked by controversies over the death of construction workers, concerns over LGBTQ+ rights, and allegations of corruption. Drawing on 573,927 tweets from UK-based accounts and 15,812 excerpts from 66 UK newspapers, we examine how political and apolitical attention to the competition was distributed across mainstream and social media, how it varied across various Twitter sub-networks, and how UK journalists navigated both arenas. Combining Topic Modelling, LLM-based classification, Social Network Analysis, and close reading, we find that political content accounted for 29.0% of tweets and 18.5% of news excerpts—a gap driven largely by Twitter discussion of issues only loosely tied to the tournament, particularly the Mahsa Amini protests in Iran. Consistent with Wright et al. (2015), we find that political talk routinely surfaced within seemingly apolitical conversations about teams, players, and matches—from debates over footballers’ working conditions to readings of Morocco’s run as a symbolic challenge to European footballing power. At the same time, political conversations on Twitter were quite segmented, with different sub-networks focusing on different political topics. Moreover, while the UK press emphasized LGBTQ+ rights and migrant worker deaths, Twitter hosted more sustained debate about perceived bias in Western media coverage of Qatar, especially within a community of users with ties to Arab countries and Islam. These insights offer unusually direct empirical support for counterpublic theory. Journalists, meanwhile, mostly used Twitter to push content but also engaged in disputes with one another over bias, accuracy, and the propriety of attending the event, reflecting a shift toward more assertive personal positioning consistent with broader trends in journalistic branding. We close by arguing that the framework of online “third spaces”—apolitical online fan environments into which politics occasionally intrudes—fits the World Cup poorly: the tournament’s scale, commercialization, and political salience make sustained apoliticism untenable, and a meta-debate over whether to “keep politics out of football” played out openly across both platforms. The concept may apply more productively to smaller, more bounded fan communities.

Misinformation is a growing concern in a decade involving critical global events. While social media regulation is mainly dedicated towards the detection and prevention of fake news and political misinformation, there is limited research about religious misinformation which has only been addressed through qualitative approaches. In this work, we study the spread of fabricated quotes (Hadith) that are claimed to belong to Prophet Muhammad (the prophet of Islam) as a case study demonstrating one of the most common religious misinformation forms on Arabic social media. We attempt through quantitative methods to understand the characteristics of social media users who interact with fabricated Hadith. We spotted users who frequently circulate fabricated Hadith and others who frequently debunk it to understand the main differences between the two groups. We used Logistic Regression to automatically predict their behaviors and analyzed its weights to gain insights about the characteristics and interests of each group. We find that both fabricated Hadith circulators and debunkers have generally a lot of ties to religious accounts. However, circulators are identified by many accounts that follow the Shia branch of Islam, Sunni Islamic public figures from the gulf countries, and many Sunni non-professional pages posting Islamic content. On the other hand, debunkers are identified by following academic Islamic scholars from multiple countries and by having more intellectual non-religious interests like charity, politics, and activism.

Arabic has diverse dialects, where one dialect can be substantially different from the others. In the NLP literature, some assumptions about these dialects are widely adopted (e.g., ``Arabic dialects can be grouped into distinguishable regional dialects") and are manifested in different computational tasks such as Arabic Dialect Identification (ADI). However, these assumptions are not quantitatively verified. We identify four of these assumptions and examine them by extending and analyzing a multi-label dataset, where the validity of each sentence in 11 different country-level dialects is manually assessed by speakers of these dialects. Our analysis indicates that the four assumptions oversimplify reality, and some of them are not always accurate. This in turn might be hindering further progress in different Arabic NLP tasks.

Hadith, the recorded words and actions of the prophet Muhammad, is a key source of the instructions and foundations of Islam, alongside the Quran. Interpreting individual hadiths and verifying their authenticity can be difficult, even controversial, and the subject has attracted the attention of many scholars who have established an entire science of Hadith criticism. Recent quantitative studies of hadiths focus on developing systems for automatic classification, authentication, and information retrieval that operate over existing hadith compilations. Qualitative studies on the other hand try to discuss different social and political issues from the perspective of hadiths, or they inspect how hadiths are used in specific contexts in official communications and press releases for argumentation and propaganda. However, there are no studies that attempt to understand the actual presence of hadiths among Muslims in their daily lives and interactions. In this study, we try to fill this gap by exploring the presence of hadiths on Twitter from January 2019 to January 2023. We highlight the challenges that quantitative methods should consider while processing texts that include hadiths and we provide a methodology for Islamic scholars to validate their hypotheses about hadiths on big data that better represent the position of the society and Hadith influence

Hamdy Mubarak, Rana Malhas, Watheq Mansour, Abubakr Mohamed, Mahmoud Fawzi, Majd Hawasly, Tamer Elsayed, Kareem Mohamed Darwish, Walid Magdy. Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks. 2025.

Amr Keleg, Sharon Goldwater, Walid Magdy. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Hadith, the recorded words and actions of the prophet Muhammad, is a key source of the instructions and foundations of Islam, alongside the Quran. Interpreting individual hadiths and verifying their authenticity can be difficult, even controversial, and the subject has attracted the attention of many scholars who have established an entire science of Hadith criticism. Recent quantitative studies of hadiths focus on developing systems for automatic classification, authentication, and information retrieval that operate over existing hadith compilations. Qualitative studies on the other hand try to discuss different social and political issues from the perspective of hadiths, or they inspect how hadiths are used in specific contexts in official communications and press releases for argumentation and propaganda. However, there are no studies that attempt to understand the actual presence of hadiths among Muslims in their daily lives and interactions. In this study, we try to fill this gap by exploring the presence of hadiths on Twitter from January 2019 to January 2023. We highlight the challenges that quantitative methods should consider while processing texts that include hadiths and we provide a methodology for Islamic scholars to validate their hypotheses about hadiths on big data that better represent the position of the society and Hadith influence on it.