2011 ATimeDependentTopicModelforMult

• (Hong et al., 2011) ⇒ Liangjie Hong, Byron Dom, Siva Gurumurthy, and Kostas Tsioutsiouliklis. (2011). “A Time-dependent Topic Model for Multiple Text Streams.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020551

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+A+Time-dependent+Topic+Model+for+Multiple+Text+Streams
• http://dl.acm.org/citation.cfm?id=2020408.2020551&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; clustering; experimentation; miscellaneous; news; temporal dynamics; text streams; theory; topic models; twitter

Abstract

In recent years social media have become indispensable tools for information dissemination, operating in tandem with traditional media outlets such as newspapers, and it has become critical to understand the interaction between the new and old sources of news. Although social media as well as traditional media have attracted attention from several research communities, most of the prior work has been limited to a single medium. In addition temporal analysis of these sources can provide an understanding of how information spreads and evolves. Modeling temporal dynamics while considering multiple sources is a challenging research problem. In this paper we address the problem of modeling text streams from two news sources - Twitter and Yahoo! News. Our analysis addresses both their individual properties (including temporal dynamics) and their inter-relationships. This work extends standard topic models by allowing each text stream to have both local topics and shared topics. For temporal modeling we associate each topic with a time-dependent function that characterizes its popularity over time. By integrating the two models, we effectively model the temporal dynamics of multiple correlated text streams in a unified framework. We evaluate our model on a large-scale dataset, consisting of text streams from both Twitter and news feeds from Yahoo! News. Besides overcoming the limitations of existing models, we show that our work achieves better perplexity on unseen data and identifies more coherent topics. We also provide analysis of finding real-world events from the topics obtained by our model.

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ATimeDependentTopicModelforMult	Byron Dom Kostas Tsioutsiouliklis Liangjie Hong Siva Gurumurthy			A Time-dependent Topic Model for Multiple Text Streams				10.1145/2020408.2020551		2011