Predicting Author Blog Channels with High Value Future Posts for Monitoring

7 Pages Posted: 14 Sep 2011 Last revised: 29 Sep 2012

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 Natural Sciences; University of Maryland - Robert H. Smith School of Business

Date Written: January 24, 2011

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 (na─▒ve) 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.

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

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: https://ssrn.com/abstract=1927096 or http://dx.doi.org/10.2139/ssrn.1927096

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
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 Natural Sciences ( email )

2300 Symons Hall,
University of Maryland
College Park, MD 20742-3255
United States

University of Maryland - Robert H. Smith School of Business

College Park, MD 20742-1815
United States

Paper statistics

Downloads
55
Rank
308,076
Abstract Views
508