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.

Immersive technologies such as 360° cameras and head-mounted displays (HMDs) have become affordable to the average consumer, opening up new audiences for storytellers. However, existing immersive post-production software often requires too great a technical or financial investment for smaller creative shops, including most of the country's newspapers and local broadcast journalism organizations. Game engines, for example, are unnecessarily complicated for simple 360° video projects. Introducing Immerj - an open-source abstraction layer simplifying the Unity3D game engine's interface for immersive content creators. Our primary collaborator, Professor R.B. Brenner, director of the School of Journalism at the University of Texas at Austin, organized hands-on demos with our team and journalists and designers from some of the top news organizations all over the country in order to follow a human-centered design process. In just over one year, a small team of undergraduate researchers at the Texas Advanced Computing Center (TACC) has created a potentially disruptive democratization of technology.