Mining Political Blogs With Network Based Topic Models

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.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 0 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.