Social media data analysis in urban e-planning

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Computational social media data analysis (SMDA) is opening up new possibilities for participatory urban planning. The aim of this study is to analyse what kind of computational methods can be used to analyse social media data to inform urban planning. A descriptive literature review of recent case study articles reveal that in this context SMDA has been applied mainly to location based social media data, such as geo-tagged Tweets, photographs and check-in data. There were only a few studies concerning the use of non-place-based data. Based on this review SMDA can provide planners with local knowledge about people's opinions, experiences, feelings, behaviour, and about the city structure. However, integration of this knowledge in planning and decision-making has not been completely successful in any of the cases. By way of a conclusion, a planning-led categorization of the SMDA method's tools and analysis results is suggested.
Original languageEnglish
Pages (from-to)18-31
Number of pages14
Issue number4
Publication statusPublished - Oct 2017
MoE publication typeA1 Journal article-refereed


  • Citizen Participation
  • Computational Methods
  • Content Analysis
  • Literature Review
  • Location Based Social Media
  • Machine Learning
  • Network Analysis
  • Spatial Data Mining
  • Text Mining
  • Urban Planning

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