Exploiting latent information to predict diffusions of novel topics on social networks
Journal
50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference
Journal Volume
2
Pages
344-348
Date Issued
2012
Author(s)
Abstract
This paper brings a marriage of two seemly unrelated topics, natural language processing (NLP) and social network analysis (SNA). We propose a new task in SNA which is to predict the diffusion of a new topic, and design a learning-based framework to solve this problem. We exploit the latent semantic information among users, topics, and social connections as features for prediction. Our framework is evaluated on real data collected from public domain. The experiments show 16% AUC improvement over baseline methods. The source code and dataset are available at http://www.csie.ntu.edu.tw/~d97944007/ dif fusion/. © 2012 Association for Computational Linguistics.
Other Subjects
Baseline methods; Latent information; Latent semantics; NAtural language processing; Public domains; Social connection; Social Network Analysis; Social Networks; Computational linguistics; Natural language processing systems; Social networking (online); Forecasting
Type
conference paper