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A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets.pdf (160.66 kB)

A Review of Models for Hydrating Large-scale Twitter Data of COVID-19-related Tweets for Transportation Research

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posted on 27.04.2020, 19:54 by Mahmoud Arafat

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.

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This is to acknowledge that there is no financial interest or benefit arisen from the direct applications of this research.

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Declaration of conflicts of interest

No conflicts of interest

Corresponding author email

mhela001@fiu.edu

Lead author country

United States

Lead author job role

PhD Student

Lead author institution

Florida International University

Human Participants

No

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