Training an Emergency-Response Image Classifier on Signal Data

Abstract

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.