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Mining Social Networks to Detect Traffic Incidents

Author

Listed:
  • Sebastián Vallejos

    (CONICET-UNICEN)

  • Diego G. Alonso

    (CONICET-UNICEN)

  • Brian Caimmi

    (CONICET-UNICEN)

  • Luis Berdun

    (CONICET-UNICEN)

  • Marcelo G. Armentano

    (CONICET-UNICEN)

  • Álvaro Soria

    (CONICET-UNICEN)

Abstract

Social networks are usually used by citizens to report or complain about traffic incidents that affect their daily mobility. Automatically finding traffic-related reports and extracting useful information from them is not a trivial task, due to the informal language used in social networks, to the lack of geographic metadata, and to the large amount of non traffic-related publications. In this article, we address this problem by combining Machine Learning and Natural Language Processing techniques. Our approach (a) filters publications that report traffic incidents in social networks, (b) extracts geographic information from the textual content of the publications, and (c) provides a broadcasting service that clusters all the reports of the same incident. We compared the performance of our approach with state of the art approaches and with a popular traffic-specific social network, obtaining promising results.

Suggested Citation

  • Sebastián Vallejos & Diego G. Alonso & Brian Caimmi & Luis Berdun & Marcelo G. Armentano & Álvaro Soria, 2021. "Mining Social Networks to Detect Traffic Incidents," Information Systems Frontiers, Springer, vol. 23(1), pages 115-134, February.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:1:d:10.1007_s10796-020-09994-3
    DOI: 10.1007/s10796-020-09994-3
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    Cited by:

    1. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    2. Christian Meske & Enrico Bunde, 2023. "Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection," Information Systems Frontiers, Springer, vol. 25(2), pages 743-773, April.

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