LExL: A Learning Approach for Local Expert Discovery on Twitter Conference Paper uri icon

abstract

  • © Springer International Publishing Switzerland 2016. In this paper, we explore a geo-spatial learning-to-rank framework for identifying local experts. Three of the key features of the proposed approach are: (i) a learning-based framework for integrating multiple factors impacting local expertise that leverages the fine-grained GPS coordinates of millions of social media users; (ii) a location-sensitive random walk that propagates crowd knowledge of a candidate’s expertise; and (iii) a comprehensive controlled study over AMT-labeled local experts on eight topics and in four cities. We find significant improvements of local expert finding versus two state-of-the-art alternatives.

author list (cited authors)

  • Niu, W., Liu, Z., & Caverlee, J.

citation count

  • 3

editor list (cited editors)

  • Ferro, N., Crestani, F., Moens, M., Mothe, J., Silvestri, F., Nunzio, G., Hauff, C., & Silvello, G.

publication date

  • March 2016