MASH: A Multiplatform Annotated Dataset for Societal Impact of Hurricane

The 2024 Atlantic Hurricane Season was one of the most active and destructive seasons in history. These hurricanes not only caused severe physical damage but also sparked widespread discussion on social and news media platforms. Existing datasets for studying social impacts of hurricanes often focus on outdated hurricanes and are limited to a single social media platform, failing to capture the broader social impact in today's diverse social media environment. To address these gaps, we present a Multiplatform Annotated Dataset for Societal Impact of Hurricane MASH that includes 126,686 relevant social media data samples from Reddit, TikTok, X, and YouTube, as well as 171 news media articles from mainstream news media. A subset of 12,669 social media data samples are annotated on three dimensions: humanitarian classes, bias classes, and misinformation classes. The dataset is complemented by an online analytics platform that can not only view hurricane-related posts and articles but also explores high-frequent keywords and user sentiment. To our best knowledge, MASH is the first large-scale, multimodal, multi-platform, and multi-dimensionally annotated hurricane dataset. We envision that MASH can contribute to the study of hurricanes' impact on society, such as disaster severity classification, fake news detection, public sentiment analysis, and bias identification.