基于2010 - 2022年社会媒体数据的日本老年司机公共话语:纵向分析。

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2025-06-16 DOI:10.2196/69321
Akito Nakanishi, Masao Ichikawa, Yukie Sano
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引用次数: 0

摘要

背景:随着全球人口老龄化,对老年司机的担忧正在加剧。尽管老年司机本身并不比其他年龄段的人更危险,但日本的传统调查显示,人们对老年司机的负面情绪持续存在。这种差异表明了分析社交媒体上的话语的重要性,在社交媒体上,公众对老年司机的看法和社会态度是积极形成的。目的:本研究旨在通过领先的社交媒体平台Twitter(随后更名为X)量化日本老年司机的长期公共话语。具体目标是:(1)检查推文中对老年司机的情绪,(2)确定推文中讨论的文本内容和主题,以及(3)分析情绪如何与各种变量相关。方法:我们收集了2010年至2022年日本与老年司机相关的推文。每个季度,我们(1)使用日语版的语言查询和单词计数词典进行情感分析,(2)采用两层非负矩阵分解进行动态主题建模,(3)应用相关分析来探索情感与崩溃率、数据计数和主题的关系。结果:我们从1,052,976个独立用户中获得了2,625,807条关于老年司机的推文。推文数量稳步增长,在2016年、2019年和2021年达到显著峰值,与高调的交通事故相吻合。情绪分析显示,消极情绪(n=383,520, 62.42%)、愤怒(n=106,767, 17.38%)、焦虑(n=114,234, 18.59%)和风险(n=357,311, 58.15%)占主导地位。主题建模确定了29个动态主题,包括与驾驶执照、碰撞事件、自动驾驶技术和交通安全相关的主题。结论:这项为期13年的研究利用Twitter数据量化了关于老年司机的公众话语,揭示了一个矛盾的现象:尽管老年司机的实际撞车率有所下降,但负面情绪和感知风险却在增加。这些发现强调了重新考虑许可政策、推广自动驾驶系统和培养更平衡的理解以减轻不当偏见和支持老年人持续安全出行的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis.

Background: As the global population ages, concerns about older drivers are intensifying. Although older drivers are not inherently more dangerous than other age groups, traditional surveys in Japan reveal persistent negative sentiments toward them. This discrepancy suggests the importance of analyzing discourse on social media, where public perceptions and societal attitudes toward older drivers are actively shaped.

Objective: This study aimed to quantify long-term public discourse on older drivers in Japan through Twitter (subsequently rebranded X), a leading social media platform. The specific objectives were to (1) examine the sentiments toward older drivers in tweets, (2) identify the textual contents and topics discussed in the tweets, and (3) analyze how sentiments correlate with various variables.

Methods: We collected Japanese tweets related to older drivers from 2010 to 2022. Each quarter, we (1) applied to the Japanese version of the Linguistic Inquiry and Word Count dictionary for sentiment analysis, (2) employed 2-layer nonnegative matrix factorization for dynamic topic modeling, and (3) applied correlation analyses to explore the relationships of sentiments with crash rates, data counts, and topics.

Results: We obtained 2,625,807 tweets from 1,052,976 unique users discussing older drivers. The number of tweets has steadily increased, with significant peaks in 2016, 2019, and 2021, coinciding with high-profile traffic crashes. Sentiment analysis revealed a predominance of negative emotions (n=383,520, 62.42%), anger (n=106,767, 17.38%), anxiety (n=114,234, 18.59%), and risk (n=357,311, 58.15%). Topic modeling identified 29 dynamic topics, including those related to driving licenses, crash events, self-driving technology, and traffic safety. The crash events topic, which increased by 0.28% per year, showed a strong correlation with negative emotion (r=0.76, P<.001) and risk (r=0.72, P<.001).

Conclusions: This 13-year study quantified public discourse on older drivers using Twitter data, revealing a paradoxical increase in negative sentiment and perceived risk, despite a decline in the actual crash rate among older drivers. These findings underscore the importance of reconsidering licensing policies, promoting self-driving systems, and fostering a more balanced understanding to mitigate undue prejudice and support continued safe mobility for older adults.

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