KAUST encouraged attendees of the 2015 Winter Enrichment Program (WEP) to share their experiences on social media using the hashtag #wep2015. The university provided tips for participants to effectively use platforms like Facebook, Twitter, and Instagram during the event. KAUST emphasized responsible sharing and respect for the university's multicultural community when posting. Why it matters: This initiative aimed to amplify the reach of WEP's activities and engage a broader audience in KAUST's community and knowledge-sharing efforts.
ArabDiscrim is a new corpus comprising 293,000 public Arabic Facebook posts from 2014 to 2024, specifically curated to discuss racism and discrimination. Unlike prior Twitter-centric datasets, it incorporates platform-native engagement signals, 200 curated terms with morphological regex families, and 20 discrimination axes. The resource also provides explicit attribution patterns and is released under a restricted research-use license for ethical compliance. Why it matters: This dataset provides a unique, ecologically valid foundation for fairness-oriented and platform-aware Arabic Natural Language Processing, moving beyond existing Twitter-centric resources.
KAUST Ph.D. student Anna Fruehstueck won a 2020 Facebook Fellowship award, a two-year fellowship from Facebook Research, focusing on computer graphics. She was selected as one of 36 recipients from over 1,800 applicants and is the University's first recipient of the scholarship. Her research explores the intersection of computer graphics, geometry processing, and visual arts using machine learning. Why it matters: This award highlights the growing prominence of KAUST in computer science research and its ability to attract and foster top talent in the field.
KAUST Associate Professor Xiangliang Zhang is using machine learning to analyze social media posts on Twitter related to COVID-19. Her team at KAUST's Computational Bioscience Research Center is analyzing sentiment in tweets using hashtags like #coronavirus and #covid19. Zhang aims to use this data to help predict localized outbreaks and provide an early warning system for governments and organizations. Why it matters: This research demonstrates the potential of AI-powered sentiment analysis to support public health efforts and inform decision-making during pandemics in the Middle East and globally.
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.
Janet Kelso from the Max Planck Institute and Sudhir Kumar from Temple University discussed evolutionary biology in a KAUST Facebook Live interview. Kelso's research focuses on interactions between modern humans and Neanderthals, finding similarities in DNA and benefits for environmental adaptation. Kumar's work, highly cited, involves big data analyses in evolutionary biology. Why it matters: The interview highlights KAUST's engagement with international experts in bioinformatics and evolutionary biology, promoting interdisciplinary research and knowledge dissemination.
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.