  The proliferation of political memes in modern information campaigns calls
for efficient solutions for image classification by ideological affiliation.
While significant advances have recently been made on text classification in
modern natural language processing literature, understanding the political
insinuation in imagery is less developed due to the hard nature of the problem.
Unlike text, where meaning arises from juxtaposition of tokens (words) within
some common linguistic structures, image semantics emerge from a much less
constrained process of fusion of visual concepts. Thus, training a model to
infer visual insinuation is possibly a more challenging problem. In this paper,
we explore an alternative unsupervised approach that, instead, infers
ideological affiliation from image propagation patterns on social media. The
approach is shown to improve the F1-score by over 0.15 (nearly 25%) over
previous unsupervised baselines, and then by another 0.05 (around 7%) in the
presence of a small amount of supervision.
