Modeling Framing in Immigration Discourse on Social Media

Julia Mendelsohn, Ceren Budak, David Jurgens


Abstract
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.
Anthology ID:
2021.naacl-main.179
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2219–2263
Language:
URL:
https://aclanthology.org/2021.naacl-main.179
DOI:
10.18653/v1/2021.naacl-main.179
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.179.pdf
Code
 juliamendelsohn/framing