利用社交媒体数据识别影响公众对无障碍性、社会经济差距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
AI总结：
本研究利用纽约市36,098条推文，通过自然语言处理和数据融合技术，识别出性别、种族、就业及环境风险等因素显著影响公众对交通无障碍性和社会经济差距的讨论倾向，并提出补贴互联网以促进弱势群体发声。
摘要：
本研究提出了一种新颖的方法，以理解影响个人对交通无障碍性、社会经济差距和公共基础设施感知的因素。与耗时且昂贵的基于调查的方法不同，该方法能够从社交媒体生成有机的大规模响应，并开发统计模型以理解个人对各种交通问题的感知。本研究检索并分析了2020年3月19日至2022年5月15日期间纽约市的36,098条推文。采用最先进的自然语言处理算法进行文本挖掘和分类。采用数据融合技术生成一系列社会经济特征，用作模型中的解释变量。模型结果显示，女性和亚裔个体倾向于比其他人更多地讨论交通无障碍性，而那些经历高邻里交通流量的人也更为直言不讳。然而，弱势个体，包括失业者、生活在低收入社区或高自然灾害风险地区的人，倾向于较少交流此类问题。至于社会经济差距，亚裔个体和经历各种空气污染的人更可能在Twitter上讨论这些话题，且通常带有负面情绪。然而，失业或弱势个体，以及生活在高自然灾害风险或预期损失地区的人，则不太倾向于发布关于此主题的推文。缺乏互联网接入可能是许多弱势个体不发布关于交通无障碍性推文的原因，而补贴互联网可能是一个可能的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
