Modeling Framing in Immigration Discourse on Social Media
This work addresses the need to analyze political discourse framing on social media for researchers in NLP and social science, though it is incremental as it applies existing methods to a new dataset.
The authors tackled the problem of understanding how ordinary people frame immigration issues on social media by creating a labeled dataset of tweets and developing supervised models to detect frames, finding that immigration-specific frames reveal ideological and regional patterns obscured by generic frames and that certain frames correlate with higher user engagement.
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.