Exploiting latent information to predict diffusions of novel topics on social networks
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
| Hi-index | 0.00 |
Recently, there has been growing interest in understanding information cascading phenomenon on popular social networks such as Face book, Twitter and Plurk. The numerous diffusion events indicate huge governmental and commercial potential. People have proposed several diffusion and cascading models based on certain assumption, but until now we do not know which one is better in predicting information propagation. In this paper, we propose a novel framework that utilizes the micro-blog data to evaluate which model is better under different circumstances. In our framework, we devise two schemes for evaluation: the direct and the indirect schemes. We conduct experiments using three diffusion models on Plurk data. The results show Independent Cascade model outperforms other diffusion models using direct scheme, while Linear Threshold model, Degree, In-Degree and Page Rank perform best using indirect scheme. The main contribution is to provide a general evaluation framework for various diffusion models.