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.

运用社交媒体和统计模型，本研究分析了纽约市的推文，发现性别、族裔、邻里交通等因素会影响个人对交通可达性的认知，而亚洲族裔和空气污染也与社会经济不平等有关。然而，对于弱势群体而言，缺乏网络可访问性可能是不善于表达交通可达性问题的原因之一。

利用社交媒体数据识别影响公众对无障碍性、社会经济差异和公共交通态度的因素

Leveraging Social Media Data to Identify Factors Influencing Public  Attitude Towards Accessibility, Socioeconomic Disparity and Public  Transportation