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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. 

When natural disasters occur, various organizations and agencies turn to social media to understand who needs help and how they have been affected. The purpose of this study is twofold: first, to evaluate whether hurricane-related tweets have some consistency over time, and second, whether Twitter-derived content is thematically similar to other private social media data. Through a unique method of using Twitter data gathered from six different hurricanes, alongside private data collected from qualitative interviews conducted in the immediate aftermath of Hurricane Harvey, we hypothesize that there is some level of stability across hurricane-related tweet content over time that could be used for better real-time processing of social media data during natural disasters. We use latent Dirichlet allocation (LDA) to derive topics, and, using Hellinger distance as a metric, find that there is a detectable connection among hurricane topics. By uncovering some persistent thematic areas and topics in disaster-related tweets, we hope these findings can help first responders and government agencies discover urgent content in tweets more quickly and reduce the amount of human intervention needed. 

Global social media use during natural disasters has been well documented (Murthy et al., 2017). In the U.S., public social media platforms are often a primary venue for those affected by disasters . Some disaster victims believe first responders will see their public posts and that the 9-1-1 telephone system becomes overloaded during crises. Moreover, some feel that the accuracy and utility of information on social media is likely higher than traditional media sources . However, sifting through content during a disaster is often difficult due to the high volume of ‘non-relevant’ content. In addition, text is studied more than images posted on Twitter, leaving a potential gap in understanding disaster experiences. Images posted on social media during disasters have a high level of complexity (Murthy et al., 2016). Our study responds to O’Neal et al.’s (2017) call-to-action that social media images posted during disasters should be studied using machine learning. 

The increasing popularity of multimedia messages shared through public or private social media spills into diverse information dissemination contexts. To date, public social media has been explored as a potential alert system during natural disasters, but high levels of noise (i.e., non-relevant content) present challenges in both understanding social experiences of a disaster and in facilitating disaster recovery. This study builds on current research by uniquely using social media data, collected in the field through qualitative interviews, to create a supervised machine learning model. Collected data represents rescuers and rescuees during the 2017 Hurricane Harvey. Preliminary findings indicate a 99% accuracy in classifying data between signal and noise for signal-to-noise ratios (SNR) of 1:1, 1:2, 1:4, and 1:8. We also find 99% accuracy in classification between respondent types (volunteer rescuer, official rescuer, and rescuee). We furthermore compare human and machine coded attributes, finding that Google Vision API is a more reliable source of detecting attributes for the training set. 

Widespread disasters can overload official agencies’ capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to harness network and mobile tools to coordinate actions and help fellow citizens. This study used semi-structured interviews and photo elicitation techniques to better understand how wide-scale rescues occurred during the 2017 Hurricane Harvey flooding in the Greater Houston, Texas USA area. We found that citizens used diverse apps and social media-related platforms during these rescues and that they played one of three roles: rescuer, dispatcher, or information compiler. The key social media coordination challenges these rescuers faced were incomplete feedback loops, unclear prioritization, and communication overload. This work-in-progress paper contributes to the field of crisis and disaster response research by sharing the nuances in how citizens use social media to respond to calls for help from flooding victims.