Paper: Exploiting Latent Information to Predict Diffusions of Novel Topics on Social Networks

ACL ID P12-2067
Title Exploiting Latent Information to Predict Diffusions of Novel Topics on Social Networks
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012
Authors

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/