Researchers from MBZUAI and other institutions presented a study at ACL 2024 on combatting misinformation by identifying misrepresented scientific research. They compiled a dataset called MISSCI, comprised of real-world examples of misinformation gathered from the HealthFeedback fact-checking website. The annotators classified the different types of errors in reasoning into nine different classes. Why it matters: This work addresses a critical need to combat the spread of scientific falsehoods online, especially given the challenges of manual fact-checking.
Michael Hickner, an Associate Professor from Penn State University, visited KAUST as part of the CRDF-KAUST-OSR Visiting Scholar Fellowship Program. Hickner specializes in Materials Science and Engineering, Chemistry, and Chemical Engineering. The visit was documented with photos by Meres J. Weche. Why it matters: Such programs foster international collaboration and knowledge exchange in science and engineering between KAUST and other leading institutions.
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.