Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper)

Authors Rahul Deb Das , Ross S. Purves

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Rahul Deb Das
  • Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
Ross S. Purves
  • Department of Geography, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland

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Rahul Deb Das and Ross S. Purves. Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 25:1-25:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


This paper explores the usefulness of Twitter data to detect traffic events and their geographical locations in India through machine learning and NLP. We develop a classification module that can identify tweets relevant for traffic authorities with 0.80 recall accuracy using a Naive Bayes classifier. The proposed model also handles vernacular geographical aspects while retrieving place information from unstructured texts using a multi-layered georeferencing module. This work shows Mumbai has a wide spread use of Twitter for traffic information dissemination with substantial geographical information contributed by the users.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
  • Information systems → Information retrieval
  • Computing methodologies → Natural language processing
  • Computing methodologies → Artificial intelligence
  • Human-centered computing → Ubiquitous and mobile computing
  • Urban mobility
  • traffic
  • UGC
  • tweet
  • event
  • GIR
  • geoparsing


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