  Timely and reliable sensing of infrastructure conditions is critical in
disaster management for planning effective infrastructure restorations. Social
media, a near real-time information source, has been widely used in disasters
for forming timely situational awareness. Yet, using social media to sense
electricity infrastructure conditions has not been explored. This study aims to
address the research gap through mining public topics from social media. To
achieve this purpose, we proposed a systematic and customized approach wherein
(1) electricity-related social media data is extracted by the classifier
developed based on Bidirectional Encoder Representations from Transformers
(BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams
to incorporate the formulaic expressions of infrastructure conditions in social
media. Electricity infrastructures in Florida impacted by Hurricane Irma are
studied for illustration and demonstration. Results show that the proposed
approach is capable of sensing the temporal evolutions and geographic
differences of electricity infrastructure conditions.
