  Polarization and echo chambers are often studied in the context of explicitly
political events such as elections, and little scholarship has examined the
mixing of political groups in non-political contexts. A major obstacle to
studying political polarization in non-political contexts is that political
leaning (i.e., left vs right orientation) is often unknown. Nonetheless,
political leaning is known to correlate (sometimes quite strongly) with many
lifestyle choices leading to stereotypes such as the "latte-drinking liberal."
We develop a machine learning classifier to infer political leaning from
non-political text and, optionally, the accounts a user follows on social
media. We use Voter Advice Application results shared on Twitter as our
groundtruth and train and test our classifier on a Twitter dataset comprising
the 3,200 most recent tweets of each user after removing any tweets with
political text. We correctly classify the political leaning of most users (F1
scores range from 0.70 to 0.85 depending on coverage). We find no relationship
between the level of political activity and our classification results. We
apply our classifier to a case study of news sharing in the UK and discover
that, in general, the sharing of political news exhibits a distinctive
left-right divide while sports news does not.
