Predicting Author Blog Channels with High Value Future Posts for Monitoring
King Abdullah University of Science and Technology
William M. Rand
University of Maryland
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
January 24, 2011
Proceedings of AAAI 2011
Robert H. Smith School Research Paper No. RHS-06-144
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
Date posted: September 14, 2011 ; Last revised: September 29, 2012
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