Abstract
This vision paper summaries the methods of using social media data (SMD) to measure urban perceptions. We highlight two major types of data sources (i.e., texts and imagery) and two corresponding techniques (i.e., natural language processing and computer vision). Recognizing the data quality issues of SMD, we propose three criteria for improving the reliability of SMD-based studies. In addition, integrating multi-source data is a promising approach to mitigating the data quality problems.
Highlights
With the rapid development of the mobile Internet, it is feasible to harvest large volumes of social media data (SMD) with spatio-temporal tags. Such geo-tagged big data provide a new approach to measuring the perceptions of different places at a collective level [12, 14]
This new research field is evolving rapidly due to the increasing visibility of social media platforms, such as Facebook, Twitter, Weibo, Foursquare, Flickr, and Yelp
Most geolocated social media posts are from densely-populated urban areas, making it possible to measure urban perceptions and represent human cognition of intra-urban spatial heterogeneity with the support of SMD [15,18]
Summary
With the rapid development of the mobile Internet, it is feasible to harvest large volumes of social media data (SMD) with spatio-temporal tags. Such geo-tagged big data provide a new approach to measuring the perceptions of different places at a collective level [12, 14]. Most geolocated social media posts are from densely-populated urban areas, making it possible to measure urban perceptions and represent human cognition of intra-urban spatial heterogeneity with the support of SMD [15,18].
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