Home > JOSIS > Vol. 2020 > No. 20 (2020)
Article Title
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Recommended Citation
Liu, Yu; Yuan, Yihong; and Zhang, Fan
(2020)
"Mining urban perceptions from social media data,"
Journal of Spatial Information Science:
No.
20, 51-55.
DOI: http://dx.doi.org/10.5311/JOSIS.2020.20.665
Available at:
https://digitalcommons.library.umaine.edu/josis/vol2020/iss20/15