  We propose a novel supervised learning approach for political ideology
prediction (PIP) that is capable of predicting out-of-distribution inputs. This
problem is motivated by the fact that manual data-labeling is expensive, while
self-reported labels are often scarce and exhibit significant selection bias.
We propose a novel statistical model that decomposes the document embeddings
into a linear superposition of two vectors; a latent neutral \emph{context}
vector independent of ideology, and a latent \emph{position} vector aligned
with ideology. We train an end-to-end model that has intermediate contextual
and positional vectors as outputs. At deployment time, our model predicts
labels for input documents by exclusively leveraging the predicted positional
vectors. On two benchmark datasets we show that our model is capable of
outputting predictions even when trained with as little as 5\% biased data, and
is significantly more accurate than the state-of-the-art. Through
crowd-sourcing we validate the neutrality of contextual vectors, and show that
context filtering results in ideological concentration, allowing for prediction
on out-of-distribution examples.
