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The winter storm Uri that occurred in February 2021 affected many regions in Canada, the United States, and Mexico. The State of Texas was severely impacted due to the failure in the electricity supply infrastructure compounded by its limited connectivity to other grid systems in the United States. The georeferenced estimation of the storm's impact is crucial for response and recovery. However, such information was not available until several months afterward, mainly due to the time-consuming and costly assessment processes. The latency to provide timely information particularly impacted people in the economically disadvantaged communities, who lack resources to ameliorate the impact of the storm. This work explores the potential for disaster impact estimation based on the analysis of instant social media content, which can provide actionable information to assist first responders, volunteers, governments, and the general public. In our prototype, a deep neural network (DNN) uses geolocated social media content (texts, images, and videos) to provide monetary assessments of the damage at zip code level caused by Uri, achieving up to 70% accuracy. In addition, the performance analysis across geographical regions shows that the fully trained model is able to estimate the damage for economically disadvantaged regions, such as West Texas. Our methods have the potential to promote social equity by guiding the deployment or recovery resources to the regions where it is needed based on damage assessment. [ABSTRACT FROM AUTHOR]