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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. 

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.