Navigating the Local Modes of Big Data: The Case of Topic Models

This chapter analyzes a corpus of 13,246 posts that were written for six political blogs during the course of the 2008 U.S. presidential election, focusing on a particular variant of LDA, the structural topic model (STM) (Roberts et al., 2014), which provides a framework to relate the corpus structure the authors do have with the inferred topical structure of the model.

Abstract

Each day humans generate massive volumes of data in a variety of different forms (Lazer et al., 2009). For example, digitized texts provide a rich source of political content through standard media sources such as newspapers, as well as newer forms of political discourse such as tweets and blog posts. In this chapter we analyze a corpus of 13,246 posts that were written for six political blogs during the course of the 2008 U.S. presidential election. But this is just one small example. An aggregator of nearly every document produced by the U.S. federal government, voxgov.com, has collected more than eight million documents from 2010–2014, including over a million tweets from members of Congress. These data open new possibilities for studies of all aspect of political life from public opinion (Hopkins and King, 2010) to political control (King, Pan, and Roberts, 2013) to political representation (Grimmer, 2013).