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Inferring the Diffusion and Evolution of Topics in Social Communities
BibTeX
@MISC{Xide_inferringthe,
author = {Cindy Xide and Lin Qiaozhu and Mei Yunliang and Jiang Jiawei and Han Shanxiang Qi},
title = {Inferring the Diffusion and Evolution of Topics in Social Communities},
year = {}
}
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Abstract
The prevailing of Web 2.0 techniques has led to the boom of various online communities, where topics are spreading ubiquitously among user-generated documents. Together with this diffusion process is the content evolution of the topics, where novel contents are introduced in by documents which adopt the topic. Unlike an explicit user behavior (e.g., buying a DVD), both the diffusion paths and the evolutionary process of a topic are implicit, making them much more challenging to be discovered. In this paper, we aim to simultaneously track the evolution of any arbitrary topic and reveal the latent diffusion paths of that topic in a social community. A novel and principled probabilistic model is proposed which casts our task as an joint inference problem, taking into consideration of textual documents, social influences, and topic evolution in a unified way. Specifically, a mixture model is introduced to model the generation of text according to the diffusion and the evolution of the topic, while the whole diffusion process is regularized with user-level social influences through a Gaussian Markov Random Field. Experiments on both synthetic data and real world data show that the discovery of topic diffusion and evolution benefits from this joint inference; and the probabilistic model we propose performs significantly better than existing methods.
Keyphrases
social community probabilistic model evolution benefit joint inference novel content social influence user-generated document gaussian markov random field mixture model real world data evolutionary process topic evolution various online community topic diffusion content evolution joint inference problem synthetic data explicit user behavior whole diffusion process diffusion process diffusion path unified way latent diffusion path user-level social influence textual document arbitrary topic
