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  • 标题:Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper)
  • 本地全文:下载
  • 作者:Rahul Deb Das ; Ross S. Purves
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2018
  • 卷号:114
  • 页码:1-7
  • DOI:10.4230/LIPIcs.GISCIENCE.2018.25
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要: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.
  • 关键词:Urban mobility; traffic; UGC; tweet; event; GIR; geoparsing
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