  We propose a graph contextualization method, pairGraphText, to study
political engagement on Facebook during the 2012 French presidential election.
It is a spectral algorithm that contextualizes graph data with text data for
online discussion thread. 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 identify
issue-centered structure, we develop pairGraphText, to analyze a network with
high-dimensional features on the interactions (i.e. text). This technique
scales to hundreds of thousands of nodes and thousands of unique words. In the
Facebook data, spectral clustering without the contextualizing text information
finds a mixture of (i) candidate and (ii) issue clusters. The contextualized
information with text data helps to separate these two structures. We conclude
by showing that the novel methodology is consistent under a statistical model.
