Discovering Political Topics in Facebook Discussion threads with Spectral Contextualization

08/23/2017 ∙ by Yilin Zhang, et al. ∙ 0

We propose a new technique, Spectral Contextualization, to study political engagement on Facebook during the 2012 French presidential election. In particular, we examine the Facebook posts of the eight leading candidates and the comments beneath these posts. We find evidence of both (i) candidate-centered structure, where citizens primarily comment on the wall of one candidate and (ii) issue-centered structure (i.e. on political topics), where citizens' attention and expression is primarily directed towards a specific set of issues (e.g. economics, immigration, etc). To discover issue-centered structure, we develop Spectral Contextualization, a novel approach to analyze a network with high-dimensional node covariates. This technique scales to hundreds of thousands of nodes and thousands of covariates. In the Facebook data, spectral clustering without any contextualizing information finds a mixture of (i) candidate and (ii) issue clusters. The contextualizing information with text data helps to separate these two structures. We conclude by showing that the novel methodology is consistent under a statistical model.

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