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Short Paper
DOI: 10.4230/LIPIcs.GISCIENCE.2018.25
Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper)

Authors: Rahul Deb Das and Ross S. Purves

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)

Abstract
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.
Cite as

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)

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@InProceedings{das_et_al:LIPIcs.GISCIENCE.2018.25,
  author =	{Das, Rahul Deb and Purves, Ross S.},
  title =	{{Towards the Usefulness of User-Generated Content to Understand Traffic Events}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{25:1--25:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.25},
  URN =		{urn:nbn:de:0030-drops-93539},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.25},
  annote =	{Keywords: Urban mobility, traffic, UGC, tweet, event, GIR, geoparsing}
}
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