  This study proposes a novel method to understand the factors affecting
individuals' perception of transport accessibility, socioeconomic disparity,
and public infrastructure. As opposed to the time consuming and expensive
survey-based approach, this method can generate organic large-scale responses
from social media and develop statistical models to understand individuals'
perceptions of various transportation issues. This study retrieved and analyzed
36,098 tweets from New York City from March 19, 2020, to May 15, 2022. A
state-of-the-art natural language processing algorithm is used for text mining
and classification. A data fusion technique has been adopted to generate a
series of socioeconomic traits that are used as explanatory variables in the
model. The model results show that females and individuals of Asian origin tend
to discuss transportation accessibility more than their counterparts, with
those experiencing high neighborhood traffic also being more vocal. However,
disadvantaged individuals, including the unemployed and those living in
low-income neighborhoods or in areas with high natural hazard risks, tend to
communicate less about such issues. As for socioeconomic disparity, individuals
of Asian origin and those experiencing various types of air pollution are more
likely to discuss these topics on Twitter, often with a negative sentiment.
However, unemployed, or disadvantaged individuals, as well as those living in
areas with high natural hazard risks or expected losses, are less inclined to
tweet about this subject. Lack of internet accessibility could be a reason why
many disadvantaged individuals do not tweet about transport accessibility and
subsidized internet could be a possible solution.
