Cluster 1 2 3 4 5 6
N 223 708 1011 1603 616 817
Dem -0.32 -0.25 -0.30 0.08 -0.05 0.06
Rep 0.01 0.04 0.03 -0.06 0.03 -0.11
Indep. 0.16 (ns)* -0.29 -0.05 0.25 (ns) 0.42 0.01 (ns)
3rd R
†
0.30 0.05 (ns) 0.05 (ns) 0.14 (ns) 0.20 (ns) -0.12 (ns)
tcot 0.01 0.01 0.01 -0.10 -0.01 -0.06
p2 -0.01 -0.01 0.01 -0.15 -0.01 0.07
(ns) denotes non-significance at the p ≤ 0.05 level
Logistic analysis performed for inclusion in each cluster individually. † All coefficients for third party left
were insignificant and so are not shown.
Table 1: Local interpretation of clusters
a series of regression analyses, to determine what behaviors predicted a user
falling in any given cluster. Because there are six clusters, six separate re-
gressions were estimated.
The regression analysis is shown in Table 1. Clusters 1 and 6 are the most
straightforward to interpret. Cluster 1 is positively related to all conserva-
tive concepts – following Republicans or members of a right-leaning third
party, and using the hashtag #tcot – and negatively correlated with each
liberal concepts – following Democratic politicians and using the hashtag
#p2 (note that the left-leaning third party following is not shown, though it
was included in each model, as no coefficients representing this behavior were
significant). Cluster 6 represents essentially the opposite set of behaviors –
following Democrats and using #p2, but less likely to follow Republicans or
use the hashtag #tcot. These clusters fit clearly within the left-right di-
chotomy into which American politics is most frequently divided. However,
there are four other clusters of political Twitter users that fall somewhere
outside of that clear spectrum. This is where a local interpretation is of most
use to our understanding of the politics on Twitter.
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