Ideology Prediction from Scarce and Biased Supervision: Learn to Disregard the “What” and Focus on the “How”!

Abstract

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 context vector independent of ideology, and a latent position vector aligned with ideology. We train an end-to-end model that has intermediate contextual and position vectors as outputs. At deployment time, our model predicts labels for input documents by exclusively leveraging the predicted position 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 crowdsourcing we validate the neutrality of contextual vectors, and show that context filtering results in ideological concentration, allowing for prediction on out-of-distribution examples.