A Review of Models for Hydrating Large-scale Twitter Data of
COVID-19-related Tweets for Transportation Research
Abstract
In response to the Coronavirus disease (COVID-19) outbreak and the
Transportation Research Board’s (TRB) urgent need for work related to
transportation and pandemics, this paper contributes with a sense of
urgency and provides a starting point for research on the topic. The
main goal of this paper is to support transportation researchers and the
TRB community during this COVID-19 pandemic by reviewing the performance
of software models used for extracting large-scale data from Twitter
streams related to COVID-19. The study extends the previous research
efforts in social media data mining by providing a review of
contemporary tools, including their computing maturity and their
potential usefulness. The paper also includes an open repository for the
processed data frames to facilitate the quick development of new
transportation research studies. The output of this work is recommended
to be used by the TRB community when deciding to further investigate
topics related to COVID-19 and social media data mining tools.