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 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 explores how AI and social media analytics can identify and track trends in Saudi Arabia across sectors such as construction, food and beverage, tourism, technology, and entertainment. The study analyzed millions of social media posts each month, classifying discussions and calculating scores to track trends. The AI-driven methodology was able to predict the emergence and growth of trends by utilizing social media data.
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.
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.
Nate Hagens from the University of Minnesota spoke at KAUST's Winter Enrichment Program (WEP) 2018 about the intersection of energy, human behavior, and economics. Hagens argued that society functions as an energy-dissipating "superorganism," with human preferences correlated with increasing energy needs. He emphasized that energy, not money, is the real capital, but global society is running out of it. Why it matters: The talk highlights the importance of viewing society through an ecological lens, particularly in the context of the GCC region's reliance on energy resources.
This paper explores the use of AI and social media analytics to detect sustainability trends in Saudi Arabia's evolving market, in line with Vision 2030. The study processes millions of social media posts, news articles, and blogs to understand sustainability trends across various sectors. The AI-driven methodology offers sector-specific and cross-sector insights, providing decision-makers with a snapshot of market shifts, and can be adapted to other regions.
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.