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Modeling Public Transportation Policy Using Macroscopic Social Media Data Mining.

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Název: Modeling Public Transportation Policy Using Macroscopic Social Media Data Mining.
Autoři: Purnama, Dwi Adi, Muzaffar Rana, Zahid Anugrah, Lumi, Distian Pingkan, Haritza, Inggil Tahta, Fadhillah, M. Arif
Zdroj: Spektrum Industri; Oct2025, Vol. 23 Issue 2, p192-204, 13p
Témata: TRANSPORTATION policy, SUSTAINABLE transportation, PUBLIC transit, CONTENT analysis, INDONESIANS, AIR pollution, SENTIMENT analysis, ELECTRIC vehicles
Geografický termín: INDONESIA, JAKARTA (Indonesia)
Abstrakt: Transportation policies must be created by the government, especially in countries with high population expansion, transportation services are used more to meet daily necessities. Conventional surveys to gauge public opinion are costly and slow; social media offers a macro-level proxy that can complement official data. This study employs large-scale online data mining to build decision support for transportation policy. We collected 19,806 Indonesia-based Twitter posts referencing public transport, private transport, sustainable mobility, and electric vehicles. After preprocessing, we fine-tuned IndoRoBERTa for sentiment classification and applied Latent Dirichlet Allocation for topic modeling. The sentiment model achieved 81.17% accuracy, with precision, recall, and F1-scores all above 0.80. Positive discourse concentrated on private vehicles, public transit, multimodal travel, and environmentally responsible practices, with many users endorsing eco-friendly private cars. Negative discourse emphasized severe air pollution, frequently attributing risk to emissions from private automobiles in Jakarta. Translating these insights into policy, we propose expanding electric-vehicle charging infrastructure, implementing vehicle buy-back/retirement programs, establishing low-emission zones, and promoting biofuels. The results demonstrate that macroscopic social media analytics can surface actionable public preferences and pain points, enabling near-real-time monitoring to inform adaptive and equity-oriented transportation policies. This framework provides a scalable approach for governments in rapidly growing contexts to align service provision with community sentiment while advancing sustainability goals. [ABSTRACT FROM AUTHOR]
Copyright of Spektrum Industri is the property of Spektrum Industri and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Modeling Public Transportation Policy Using Macroscopic Social Media Data Mining.
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  Data: <searchLink fieldCode="AR" term="%22Purnama%2C+Dwi+Adi%22">Purnama, Dwi Adi</searchLink><br /><searchLink fieldCode="AR" term="%22Muzaffar+Rana%2C+Zahid+Anugrah%22">Muzaffar Rana, Zahid Anugrah</searchLink><br /><searchLink fieldCode="AR" term="%22Lumi%2C+Distian+Pingkan%22">Lumi, Distian Pingkan</searchLink><br /><searchLink fieldCode="AR" term="%22Haritza%2C+Inggil+Tahta%22">Haritza, Inggil Tahta</searchLink><br /><searchLink fieldCode="AR" term="%22Fadhillah%2C+M%2E+Arif%22">Fadhillah, M. Arif</searchLink>
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  Data: Spektrum Industri; Oct2025, Vol. 23 Issue 2, p192-204, 13p
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  Data: <searchLink fieldCode="DE" term="%22TRANSPORTATION+policy%22">TRANSPORTATION policy</searchLink><br /><searchLink fieldCode="DE" term="%22SUSTAINABLE+transportation%22">SUSTAINABLE transportation</searchLink><br /><searchLink fieldCode="DE" term="%22PUBLIC+transit%22">PUBLIC transit</searchLink><br /><searchLink fieldCode="DE" term="%22CONTENT+analysis%22">CONTENT analysis</searchLink><br /><searchLink fieldCode="DE" term="%22INDONESIANS%22">INDONESIANS</searchLink><br /><searchLink fieldCode="DE" term="%22AIR+pollution%22">AIR pollution</searchLink><br /><searchLink fieldCode="DE" term="%22SENTIMENT+analysis%22">SENTIMENT analysis</searchLink><br /><searchLink fieldCode="DE" term="%22ELECTRIC+vehicles%22">ELECTRIC vehicles</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22INDONESIA%22">INDONESIA</searchLink><br /><searchLink fieldCode="DE" term="%22JAKARTA+%28Indonesia%29%22">JAKARTA (Indonesia)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Transportation policies must be created by the government, especially in countries with high population expansion, transportation services are used more to meet daily necessities. Conventional surveys to gauge public opinion are costly and slow; social media offers a macro-level proxy that can complement official data. This study employs large-scale online data mining to build decision support for transportation policy. We collected 19,806 Indonesia-based Twitter posts referencing public transport, private transport, sustainable mobility, and electric vehicles. After preprocessing, we fine-tuned IndoRoBERTa for sentiment classification and applied Latent Dirichlet Allocation for topic modeling. The sentiment model achieved 81.17% accuracy, with precision, recall, and F1-scores all above 0.80. Positive discourse concentrated on private vehicles, public transit, multimodal travel, and environmentally responsible practices, with many users endorsing eco-friendly private cars. Negative discourse emphasized severe air pollution, frequently attributing risk to emissions from private automobiles in Jakarta. Translating these insights into policy, we propose expanding electric-vehicle charging infrastructure, implementing vehicle buy-back/retirement programs, establishing low-emission zones, and promoting biofuels. The results demonstrate that macroscopic social media analytics can surface actionable public preferences and pain points, enabling near-real-time monitoring to inform adaptive and equity-oriented transportation policies. This framework provides a scalable approach for governments in rapidly growing contexts to align service provision with community sentiment while advancing sustainability goals. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Spektrum Industri is the property of Spektrum Industri and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.12928/si.v23i2.327
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 13
        StartPage: 192
    Subjects:
      – SubjectFull: INDONESIA
        Type: general
      – SubjectFull: JAKARTA (Indonesia)
        Type: general
      – SubjectFull: TRANSPORTATION policy
        Type: general
      – SubjectFull: SUSTAINABLE transportation
        Type: general
      – SubjectFull: PUBLIC transit
        Type: general
      – SubjectFull: CONTENT analysis
        Type: general
      – SubjectFull: INDONESIANS
        Type: general
      – SubjectFull: AIR pollution
        Type: general
      – SubjectFull: SENTIMENT analysis
        Type: general
      – SubjectFull: ELECTRIC vehicles
        Type: general
    Titles:
      – TitleFull: Modeling Public Transportation Policy Using Macroscopic Social Media Data Mining.
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            NameFull: Purnama, Dwi Adi
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            NameFull: Muzaffar Rana, Zahid Anugrah
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              M: 10
              Text: Oct2025
              Type: published
              Y: 2025
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