  The framing of political issues can influence policy and public opinion. Even
though the public plays a key role in creating and spreading frames, little is
known about how ordinary people on social media frame political issues. By
creating a new dataset of immigration-related tweets labeled for multiple
framing typologies from political communication theory, we develop supervised
models to detect frames. We demonstrate how users' ideology and region impact
framing choices, and how a message's framing influences audience responses. We
find that the more commonly-used issue-generic frames obscure important
ideological and regional patterns that are only revealed by
immigration-specific frames. Furthermore, frames oriented towards human
interests, culture, and politics are associated with higher user engagement.
This large-scale analysis of a complex social and linguistic phenomenon
contributes to both NLP and social science research.
