The Joint Inference of Topic Diffusion and Evolution in Social Communities

@article{Lin2011TheJI,
  title={The Joint Inference of Topic Diffusion and Evolution in Social Communities},
  author={Cindy Xide Lin and Qiaozhu Mei and Jiawei Han and Yunliang Jiang and Marina Danilevsky},
  journal={2011 IEEE 11th International Conference on Data Mining},
  year={2011},
  pages={378-387}
}
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… 

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References

SHOWING 1-10 OF 35 REFERENCES

PET: a statistical model for popular events tracking in social communities

This paper formally defines the problem of popular event tracking in online communities (PET) and proposes a novel statistical method that models the the popularity of events over time, taking into consideration the burstiness of user interest, information diffusion on the network structure, and the evolution of textual topics.

iTopicModel: Information Network-Integrated Topic Modeling

A novel topic modeling framework is proposed, which builds a unified generative topic model that is able to consider both text and structure information for documents, and a graphical model is proposed to describe the generative model.

Topic modeling with network regularization

A novel solution to the problem of topic modeling with network structure (TMN) is proposed, which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data.

Inferring Networks of Diffusion and Influence

This work develops an efficient approximation algorithm that scales to large datasets and finds provably near-optimal networks for tracing paths of diffusion and influence through networks and inferring the networks over which contagions propagate.

Learning influence probabilities in social networks

This paper proposes models and algorithms for learning the model parameters and for testing the learned models to make predictions, and develops techniques for predicting the time by which a user may be expected to perform an action.

Selecting Information Diffusion Models over Social Networks for Behavioral Analysis

This work investigates how well different information diffusion models can explain observation data by learning their parameters and discusses which model is better suited to which topic and applies these methods to behavioral analysis of topic propagation.

Group formation in large social networks: membership, growth, and evolution

It is found that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure, and decision-tree techniques are used to identify the most significant structural determinants of these properties.

Modeling hidden topics on document manifold

This paper proposes a novel algorithm called Laplacian Probabilistic Latent Semantic Indexing (LapPLSI) for topic modeling, which models the document space as a submanifold embedded in the ambient space and directly performs the topic modeling on this document manifold in question.

Learning information diffusion process on the web

Results show that LIDPW does benefit users to monitor the information diffusion process of a specific topic, and aid them to discover the diffusion start and diffusion center of the topic.

Information diffusion through blogspace

A macroscopic characterization of topic propagation through the authors' corpus is presented, formalizing the notion of long-running "chatter" topics consisting recursively of "spike" topics generated by outside world events, or more rarely, by resonances within the community.