Location-Sensitive User Profiling Using Crowdsourced Labels


  • Wei Niu Texas A&M University
  • James Caverlee Texas A&M University
  • Haokai Lu Texas A&M University




user profiling, location-sensitive


In this paper, we investigate the impact of spatial variation on the construction of location-sensitive user profiles. We demonstrate evidence of spatial variation over a collection of Twitter Lists, wherein we find that crowdsourced labels are constrained by distance. For example, that energy in San Francisco is more associated with the green movement, whereas in Houston it is more associated with oil and gas. We propose a three-step framework for location-sensitive user profiling: first, it constructs a crowdsourced label similarity graph, where each labeler and labelee are annotated with a geographic coordinate; second, it transforms this similarity graph into a directed weighted tree that imposes a hierarchical structure over these labels; third, it embeds this location-sensitive folksonomy into a user profile ranking algorithm that outputs a ranked list of candidate labels for a partially observed user profile. Through extensive experiments over a Twitter list dataset, we demonstrate the effectiveness of this location-sensitive user profiling.




How to Cite

Niu, W., Caverlee, J., & Lu, H. (2018). Location-Sensitive User Profiling Using Crowdsourced Labels. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11261