A paper from Professor Dong Wang's Social Sensing & Intelligence Lab received the Best Dataset Paper Award at the International AAAI Conference on Web and Social Media (ICWSM) held in May 2026 in Los Angeles, California. According to Wang, the paper was accepted in the first review round, which had an acceptance rate of 4.7 percent (14 of 298 submissions).
"MASH: A Multiplatform and Multimodal Annotated Dataset for Societal Impact of Hurricane" was co-authored by iSchool PhD students Ruichen Yao, Yifan Liu, Yaokun Liu; Informatics PhD student Zelin Li; Raaghav Pillai, an undergraduate in Computer Science and Statistics; Lanyu Shang (PhD '23), Loyola Marymount University; Yang Zhang, Miami University; Na Wei and Ximing Cai, Department of Civil and Environmental Engineering, University of Illinois; Aslanbek Murzakhmetov, M.Kh. Dulaty Taraz University, Kazakhstan; Aliya Maussymbayeva, Abylkas Saginov Karaganda Technical University, Kazakhstan; and Wang.
"I'm especially happy for the students and collaborators involved, who put a tremendous amount of effort into the project. It is rewarding to see their work recognized at such a competitive venue," said Wang.
In the paper, the researchers introduce MASH, a new large-scale research dataset built from social media posts related to hurricanes that affected the United States in 2024. Unlike earlier datasets that often focus on a single platform or historical events, MASH brings together content from multiple major platforms, including Reddit, TikTok, and YouTube, to capture a broader picture of how people share information and experiences during disasters. The dataset also incorporates text, images, and videos. The dataset is designed to support future research on disaster response, public sentiment, policy planning, and the role of online information during emergencies.