Exploiting latent information to predict diffusions of novel topics on social networks

Authors:
Tsung-Ting Kuo;San-Chuan Hung;Wei-Shih Lin;Nanyun Peng;Shou-De Lin;Wei-Fen Lin
Affiliations:
National Taiwan University, Taiwan;National Taiwan University, Taiwan;National Taiwan University, Taiwan;National Taiwan University, Taiwan;National Taiwan University, Taiwan;MobiApps Corporation, Taiwan
Venue:
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Year:
2012

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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/diffusion/