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Mining Political Blogs With Network Based Topic Models

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Date
2014
Author
Liang, Jiawei
Advisor
Banks, David L
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Abstract

We develop a Network Based Topic Model (NBTM), which integrates a Random

Graph model with the Latent Dirichlet Allocation (LDA) model. The NBTM assumes that the topic proportion of a document has a xed variance across the document corpus with author dierences treated as random eects. It also assumes that the links between documents are binary variables whose probabilities depend upon the author random eects. We t the model to political blog posts during the calendar year 2012 that mention Trayvon Martin. This paper presents the topic extraction results and posterior prediction results for hidden links within the blogosphere.

Type
Master's thesis
Department
Statistical and Economic Modeling
Subject
Statistics
Political Science
Permalink
https://hdl.handle.net/10161/8860
Citation
Liang, Jiawei (2014). Mining Political Blogs With Network Based Topic Models. Master's thesis, Duke University. Retrieved from https://hdl.handle.net/10161/8860.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

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