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Accepted for/Published in: JMIR Infodemiology

Date Submitted: Nov 26, 2024
Open Peer Review Period: Jan 2, 2025 - Feb 27, 2025
Date Accepted: Apr 22, 2025
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis

Nakanishi A, Ichikawa M, Sano Y

Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis

JMIR Infodemiology 2025;5:e69321

DOI: 10.2196/69321

PMID: 40523233

PMCID: 12209477

Public Discourse Toward Older Drivers in Japan: Longitudinal Analysis of Social Media Data from 2010 to 2022

  • Akito Nakanishi; 
  • Masao Ichikawa; 
  • Yukie Sano

ABSTRACT

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 aims to quantify long-term public discourse on older drivers in Japan through Twitter, a leading social media platform. The specific objectives are 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 are associated with these textual contents.

Methods:

We collected Japanese tweet related to older drivers from 2010 to 2022. Each quarter, we (1) applied to the J-LIWC and J-MFD dictionaries for sentiment analysis, (2) employed two-layer Non-negative Matrix Factorization for dynamic topic modeling, and (3) applied logistic regression analyses to explore the relationships between sentiments 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 a significant peak in 2016, 2019, and 2021, coinciding with high-profile traffic crashes. Sentiment analysis revealed a predominance of negative emotion (62.4%), anger (17.4%), anxiety (18.6%), and risk (58.2%). Topic modeling identified 29 dynamic topics, including those related to driving licenses, traffic crashes, personal perspectives, and traffic issues. The Crash events topic, which increased by 0.08% per quarter, showed a strong correlation with negative emotion (B = 23.39, P < .001) and risk (B = 24.1, 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 more accurate communication about the crashed caused by older drivers to mitigate undue prejudice and avoid unnecessary disadvantages for them.


 Citation

Please cite as:

Nakanishi A, Ichikawa M, Sano Y

Public Discourse Toward Older Drivers in Japan Using Social Media Data From 2010 to 2022: Longitudinal Analysis

JMIR Infodemiology 2025;5:e69321

DOI: 10.2196/69321

PMID: 40523233

PMCID: 12209477

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