TM-LDA: efficient online modeling of latent topic transitions in social media
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of correlated sequential patterns based on null hypothesis
Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning
Co-factorization machines: modeling user interests and predicting individual decisions in Twitter
Proceedings of the sixth ACM international conference on Web search and data mining
Diffusion of innovations revisited: from social network to innovation network
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A unified generative model for characterizing microblogs' topics
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Deep Twitter diving: exploring topical groups in microblogs at scale
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
A time decoupling approach for studying forum dynamics
World Wide Web
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The prevalence of Web 2.0 techniques has led to the boom of various online communities, where topics spread ubiquitously among user-generated documents. Working together with this diffusion process is the evolution of topic content, where novel contents are introduced by documents which adopt the topic. Unlike explicit user behavior (e.g., buying a DVD), both the diffusion paths and the evolutionary process of a topic are implicit, making their discovery challenging. In this paper, we track the evolution of an 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, which considers 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.