Fudan University's Zhongyu Wei presented research on social simulation driven by LLMs, covering individual and large-scale social movement simulation. Wei directs the Data Intelligence and Social Computing Lab (Fudan DISC) and has published extensively on multimodal large models and social computing. His work includes the Volcano multimodal model, DISC-MedLLM, and ElectionSim. Why it matters: Using LLMs for social simulation could provide new tools for understanding and potentially predicting social dynamics in the Arab world.
A new mini-batch strategy using aggregated relational data is proposed to fit the mixed membership stochastic blockmodel (MMSB) to large networks. The method uses nodal information and stochastic gradients of bipartite graphs for scalable inference. The approach was applied to a citation network with over two million nodes and 25 million edges, capturing explainable structure. Why it matters: This research enables more efficient community detection in massive networks, which is crucial for analyzing complex relationships in various domains, but this article has no clear connection to the Middle East.
This paper reflects on two decades of building NLP resources and research infrastructure for Arabic, an historically underserved language. The first decade focused on foundational linguistic infrastructure, while the second shifted towards computational social science and socially oriented applications. The authors highlight three lessons: dataset building is a social process, communities often matter more than shared tasks, and computational social science exposes challenges beyond traditional NLP training. Why it matters: The paper argues that the most difficult problems in developing NLP for underserved communities are social, institutional, and epistemic, offering critical insights for future research directions in Arabic AI.
MBZUAI researchers introduce SocialMaze, a new benchmark for evaluating social reasoning capabilities in large language models (LLMs). SocialMaze includes six diverse tasks across social reasoning games, daily-life interactions, and digital community platforms, emphasizing deep reasoning, dynamic interaction, and information uncertainty. Experiments show that LLMs vary in handling dynamic interactions, degrade under uncertainty, but can be improved via fine-tuning on curated reasoning examples.
A KAUST research team is using cellphone mobility data, Google searches, and social media to model and predict COVID-19 spread. The models aim to forecast cases in the coming weeks and inform resource allocation, including hospital beds and medical staff. The team is using aggregated and anonymized data from cellphone companies to respect people's privacy. Why it matters: Integrating real-time digital data with epidemiological modeling can improve the speed and effectiveness of public health responses in the region and globally.
This paper introduces a new task: detecting propaganda techniques in code-switched text. The authors created and released a corpus of 1,030 English-Roman Urdu code-switched texts annotated with 20 propaganda techniques. Experiments show the importance of directly modeling multilinguality and using the right fine-tuning strategy for this task.
Ekaterina Vylomova from the University of Melbourne gave a talk on using NLP models to advance research in linguistic morphology, typology, and social psychology. The talk covered using models to study morphology, phonetic changes in words over time, and diachronic changes in language semantics. Vylomova presented the UniMorph project, a cross-lingual annotation schema and database with morphological paradigms for over 150 languages. Why it matters: This research demonstrates the potential of NLP to contribute to a deeper understanding of language evolution and structure, with applications in linguistic research and the study of social and cultural changes.
Researchers have introduced JobArabi, a new large-scale corpus consisting of 20,528 Arabic job announcements collected from X between January 2024 and October 2025. The dataset was compiled using a linguistically informed query framework covering various Arabic recruitment expressions, offering metadata like timestamps and geolocation for detailed analysis. Quantitative analysis of JobArabi reveals sociolinguistic patterns, including persistent gendered hiring language, regional occupational demand variations, and emotional framing in recruitment messages. Why it matters: This corpus provides a valuable resource for research in Arabic NLP, computational social science, and digital labor studies, offering unique insights into labor market communication and linguistic change in the Arab world.