@inproceedings{mendelsohn-etal-2021-modeling,
title = "Modeling Framing in Immigration Discourse on Social Media",
author = "Mendelsohn, Julia and
Budak, Ceren and
Jurgens, David",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.179",
doi = "10.18653/v1/2021.naacl-main.179",
pages = "2219--2263",
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.",
}
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%0 Conference Proceedings
%T Modeling Framing in Immigration Discourse on Social Media
%A Mendelsohn, Julia
%A Budak, Ceren
%A Jurgens, David
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F mendelsohn-etal-2021-modeling
%X 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.
%R 10.18653/v1/2021.naacl-main.179
%U https://aclanthology.org/2021.naacl-main.179
%U https://doi.org/10.18653/v1/2021.naacl-main.179
%P 2219-2263
Markdown (Informal)
[Modeling Framing in Immigration Discourse on Social Media](https://aclanthology.org/2021.naacl-main.179) (Mendelsohn et al., NAACL 2021)
ACL
- Julia Mendelsohn, Ceren Budak, and David Jurgens. 2021. Modeling Framing in Immigration Discourse on Social Media. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2219–2263, Online. Association for Computational Linguistics.