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.
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.
A panel discussion hosted by MBZUAI in collaboration with the Manara Center for Coexistence and Dialogue addressed misinformation and its threat to elections. The talk covered the reasons behind the rise of misinformation, citizen perspectives, and the role of social media influencers. Two cases, the Indian general elections of 2024 and the upcoming US presidential elections in November 2024, were used to describe the contours of misinformation. Why it matters: Understanding the dynamics of misinformation, especially through social media influencers, is crucial for safeguarding democratic processes in the region and globally.
A new framework for constructing confidence sets for causal orderings within structural equation models (SEMs) is presented. It leverages a residual bootstrap procedure to test the goodness-of-fit of causal orderings, quantifying uncertainty in causal discovery. The method is computationally efficient and suitable for medium-sized problems while maintaining theoretical guarantees as the number of variables increases. Why it matters: This offers a new dimension of uncertainty quantification that enhances the robustness and reliability of causal inference in complex systems, but there is no indication of connection to the Middle East.
KAUST hosted the KAUST Research Conference: Advances in Well Construction with Focus on Near-Wellbore Physics and Chemistry from November 7 to 9. The conference was co-chaired by Eric van Oort, a professor at UT Austin, and Tadeusz Patzek, director of the University’s Upstream Petroleum Engineering Research Center. Attendees included professors from the University of Queensland and UT Austin, and directors from GenesisRTS and Labyrinth Consulting Services, Inc. Why it matters: The conference facilitates international collaboration on advancements in petroleum engineering and well construction technologies, which are strategically important for Saudi Arabia.
A study analyzes spam content on trending hashtags on Saudi Twitter, finding that approximately 75% of the total generated content is spam. The paper assesses the performance of previous spam detection systems on a newly gathered dataset and proposes an updated manual classification algorithm to improve accuracy. Adapted features are used to build a new data-driven detection system to respond to spammers' evolving techniques. Why it matters: The high prevalence of spam in Arabic content on Twitter necessitates the development of adaptive detection techniques to maintain the quality and trustworthiness of online information in the region.
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.
This study investigates the correlation between Google Trends data for COVID-19 symptoms and the actual number of COVID-19 cases in Saudi Arabia between March and October 2020. The researchers found that searches for "cough" and "sore throat" were most frequent, while "loss of smell", "loss of taste", and "diarrhea" showed the highest correlation with confirmed cases. The study concludes that Google searches can serve as a supplementary surveillance tool for monitoring the spread of COVID-19 in Saudi Arabia. Why it matters: The research demonstrates the potential of using readily available digital data to augment traditional surveillance methods for public health monitoring in the region.