Spatio-temporal meme prediction Conference Paper uri icon


  • In this paper, we tackle the problem of predicting what online memes will be popular in what locations. Specifically, we develop data-driven approaches building on the global footprint of 755 million geo-tagged hashtags spread via Twitter. Our proposed methods model the geo-spatial propagation of online information spread to identify which hashtags will become popular in specific locations. Concretely, we develop a novel reinforcement learning approach that incrementally updates the best geo-spatial model. In experiments, we find that the proposed method outperforms alternative linear regression based methods. Copyright 2013 ACM.

author list (cited authors)

  • Kamath, K. Y., & Caverlee, J.

citation count

  • 10

editor list (cited editors)

  • He, Q. i., Iyengar, A., Nejdl, W., Pei, J., & Rastogi, R.

publication date

  • January 2013