| Title |
Location-Centric Social Media Analytics: Challenges and Opportunities for Smart Cities |
| ID_Doc |
41240 |
| Authors |
Yang, DQ; Qu, BQ; Cudre-Mauroux, P |
| Title |
Location-Centric Social Media Analytics: Challenges and Opportunities for Smart Cities |
| Year |
2021 |
| Published |
Ieee Intelligent Systems, 36, 5 |
| DOI |
10.1109/MIS.2020.3009438 |
| Abstract |
With the proliferation of increasingly powerful smartphones, location-centric social media platforms, such as Foursquare, have attracted millions of users sharing their physical activity online, resulting in an invaluable source of fine-grained, semantically rich, and spatiotemporal user activity data. Such data provides us with an unprecedented opportunity for analyzing urban dynamics and developing smart city applications. In this article, we first systematically discuss the unique characteristics of location-centric social media data, which consist of four data dimensions, i.e., spatial, temporal, semantic, and social dimensions. We then highlight three key challenges relating to data analytics, i.e., data heterogeneity, data quality, and privacy. Finally, we discuss the opportunities of leveraging location-centric social media data for urban analytics and smart cities, including both data analytics within and across the four data dimensions, and data fusion with further urban data. |
| Author Keywords |
Social network services; Media; Data analysis; Smart cities; Data privacy; Spatiotemporal phenomena; Semantics; Location-centric social media; urban analytics; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000711734100005 |
| WoS Category |
Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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