  Warning: Contents of this paper may be upsetting.
  Public attitudes towards key societal issues, expressed on online media, are
of immense value in policy and reform efforts, yet challenging to understand at
scale. We study one such social issue: homelessness in the U.S., by leveraging
the remarkable capabilities of large language models to assist social work
experts in analyzing millions of posts from Twitter. We introduce a framing
typology: Online Attitudes Towards Homelessness (OATH) Frames: nine
hierarchical frames capturing critiques, responses and perceptions. We release
annotations with varying degrees of assistance from language models, with
immense benefits in scaling: 6.5x speedup in annotation time while only
incurring a 3 point F1 reduction in performance with respect to the domain
experts. Our experiments demonstrate the value of modeling OATH-Frames over
existing sentiment and toxicity classifiers. Our large-scale analysis with
predicted OATH-Frames on 2.4M posts on homelessness reveal key trends in
attitudes across states, time periods and vulnerable populations, enabling new
insights on the issue. Our work provides a general framework to understand
nuanced public attitudes at scale, on issues beyond homelessness.
