DOI

  • Manjira Sinha
  • Xiangnan He
  • Alessandro Bozzon
  • Sandya Mannarswamy
  • Pradeep Murukannaiah
  • Tridib Mukherjee
In an increasingly digital urban setting, connected & concerned Citizens typically voice their opinions on various civic topics via social media. Efficient and scalable analysis of these citizen voices on social media to derive actionable insights is essential to the development of smart cities. The very nature of the data: heterogeneity and dynamism, the scarcity of gold standard annotated corpora, and the need for multi-dimensional analysis across space, time and semantics, makes urban social media analytics challenging. This workshop is dedicated to the theme of social media analytics for smart cities, with the aim of focusing the interest of CIKM research community on the challenges in mining social media data for urban informatics. The workshop hopes to foster collaboration between researchers working in information retrieval, social media analytics, linguistics; social scientists, and civic authorities, to develop scalable and practical systems for capturing and acting upon real world issues of cities as voiced by their citizens in social media. The aim of this workshop is to encourage researchers to develop techniques for urban analytics of social media data, with specific focus on applying these techniques to practical urban informatics applications for smart cities.
Original languageEnglish
Title of host publicationCIKM'17 Proceedings of the 2017 ACM Conference on Information and Knowledge Management
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Pages2567-2568
Number of pages2
ISBN (Electronic)978-1-4503-4918-5
DOIs
StatePublished - 2017
EventThe 2017 ACM on Conference on Information and Knowledge Management - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Conference

ConferenceThe 2017 ACM on Conference on Information and Knowledge Management
Abbreviated titleCIKM'17
CountrySingapore
CitySingapore
Period6/11/1710/11/17

    Research areas

  • Urban Informatics, Social Media Analytics, Text Mining

ID: 34604195