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Vast amounts of damage data exist following natural disasters; the difficulty is collecting these perishable data in a timely fashion and curating them in a way that facilitates their use in follow-on research. Traditional on-the-ground reconnaissance efforts tend to focus limited human and financial resources on collecting and documenting detailed data of the most severe damage, typically in relatively small geographic areas immediately following an event. To improve post-event loss predictions, it is imperative that we collect broad and robust damage datasets, including details on good and poor performance and performance in small-to-moderate events. This presentation will introduce a framework to proactively engage local journalists and citizen scientists to collect vast amounts of real-world observational data via social media networks, such that valuable time, money, and engineering expertise can be focused on interpreting the citizen science data and on investigating the most interesting observations of damage more fully. The presentation will discuss how parts of this framework are being implemented in a National Science Foundation RAPID Response Research project to collect and curate social media data from Hurricane Florence and what future work is necessary to fully realize this citizen scientist-enabled framework for developing robust damage datasets for the natural hazards research community.