2013 ExtractingSocialEventsforLearni

• (Lin et al., 2013) ⇒ Shuyang Lin, Fengjiao Wang, Qingbo Hu, and Philip S. Yu. (2013). “Extracting Social Events for Learning Better Information Diffusion Models.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487584

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Extracting+Social+Events+for+Learning+Better+Information+Diffusion+Models
• http://dl.acm.org/citation.cfm?id=2487575.2487584&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; information diffusion; social event; social influence

Abstract

Learning of the information diffusion model is a fundamental problem in the study of information diffusion in social networks. Existing approaches learn the diffusion models from events in social networks. However, events in social networks may have different underlying reasons. Some of them may be caused by the social influence inside the network, while others may reflect external trends in the “real world. Most existing work on the learning of diffusion models does not distinguish the events caused by the social influence from those caused by external trends.

In this paper, we extract social events from data streams in social networks, and then use the extracted social events to improve the learning of information diffusion models. We propose a LADP (Latent Action Diffusion Path) model to incorporate the information diffusion model with the model of external trends, and then design an EM-based algorithm to infer the diffusion probabilities, the external trends and the sources of events efficiently.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 ExtractingSocialEventsforLearni	Philip S. Yu Fengjiao Wang Shuyang Lin Qingbo Hu			Extracting Social Events for Learning Better Information Diffusion Models				10.1145/2487575.2487584		2013