Abstract

http://ssrn.com/abstract=1927096
 
 

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Predicting Author Blog Channels with High Value Future Posts for Monitoring


Shanchan Wu


HP Labs

Tamer Elsayed


King Abdullah University of Science and Technology

William M. Rand


University of Maryland

Louiqa Raschid


University of Maryland - Decision and Information Technologies Department; University of Maryland - College of Computer, Mathematical and Physical Sciences; University of Maryland - Robert H. Smith School of Business

January 24, 2011

Proceedings of AAAI 2011
Robert H. Smith School Research Paper No. RHS-06-144

Abstract:     
The phenomenal growth of social media, both in scale and importance, has created a unique opportunity to track information diffusion and the spread of influence, but can also make efficient tracking difficult. Given data streams representing blog posts on multiple blog channels and a focal query post on some topic of interest, our objective is to predict which of those channels are most likely to contain a future post that is relevant, or similar, to the focal query post. We denote this task as the future author prediction problem (FAPP). This problem has applications in information diffusion for brand monitoring and blog channel personalization and recommendation. We develop prediction methods inspired by (naıve) information retrieval approaches that use historical posts in the blog channel for prediction. We also train a ranking support vector machine (SVM) to solve the problem. We evaluate our methods on an extensive social media dataset; despite the difficulty of the task, all methods perform reasonably well. Results show that ranking SVM prediction can exploit blog channel and diffusion characteristics to improve prediction accuracy. Moreover, it is surprisingly good for prediction in emerging topics and identifying inconsistent authors.

Number of Pages in PDF File: 7

Keywords: social media, blog, prediction, support vector machine

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Date posted: September 14, 2011 ; Last revised: September 29, 2012

Suggested Citation

Wu, Shanchan and Elsayed, Tamer and Rand, William M. and Raschid, Louiqa, Predicting Author Blog Channels with High Value Future Posts for Monitoring (January 24, 2011). Proceedings of AAAI 2011; Robert H. Smith School Research Paper No. RHS-06-144. Available at SSRN: http://ssrn.com/abstract=1927096 or http://dx.doi.org/10.2139/ssrn.1927096

Contact Information

Shanchan Wu
HP Labs ( email )
Palo Alto, CA
United States
Tamer Elsayed
King Abdullah University of Science and Technology ( email )
Saudi Arabia
William M. Rand (Contact Author)
University of Maryland ( email )
College Park, MD 20742
United States
Louiqa Raschid
University of Maryland - Decision and Information Technologies Department ( email )
Robert H. Smith School of Business
4313 Van Munching Hall
College Park, MD 20815
United States
University of Maryland - College of Computer, Mathematical and Physical Sciences ( email )
College Park, VA 20742-3255
United States
University of Maryland - Robert H. Smith School of Business
College Park, MD 20742-1815
United States
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