Hydrating Large-Scale Coronavirus Pandemic Tweets: A Review of Software for Transportation Research

摘要

The coronavirus (COVID-19) pandemic has challenged the established societal structure, and the transportation sector is not out of this new normal. The primary objective of this research is to analyze and review the performance of software models used for extracting and processing large-scale data from Twitter streams related to COVTD-19. The study extends the previous research efforts of machine learning applications on social media by providing a review of contemporary tools, including their computing maturity, and their potential usefulness. The paper also provides an open data repository for the processed data frames to facilitate the swift development of new transportation research. Transportation researchers and the American Society of Civil Engineers (ASCE) community are believed to benefit from this study.