Modeling Framing in Immigration Discourse on Social Media

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2219–2263 June 6–11, 2021. ©2021 Association for Computational Linguistics 2219 Modeling Framing in Immigration Discourse on Social Media Julia Mendelsohn University of Michigan juliame@umich.edu Ceren Budak University of Michigan cbudak@umich.edu David Jurgens University of Michigan jurgens@umich.edu Abstract The framing of political issues can influence policy and public opinion. Even though the public plays a key role in creating and spread- ing 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 the- ory, 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 re- sponses. We find that the more commonly- used issue-generic frames obscure important ideological and regional patterns that are only revealed by immigration-specific frames. Fur- thermore, frames oriented towards human in- terests, culture, and politics are associated with higher user engagement. This large-scale anal- ysis of a complex social and linguistic phe- nomenon contributes to both NLP and social science research. 1 Introduction Framing selects particular aspects of an issue and makes them salient in communicating a message (Entman, 1993). Framing can impact how people understand issues, attribute responsibility (Iyengar, 1991), and endorse possible solutions, thus having major implications for public opinion and policy de- cisions (Chong and Druckman, 2007). While past work has studied framing by the news media and the political elite, little is known about how ordi- nary people frame political issues. Yet, framing by ordinary people can influence others’ perspectives and may even shape elites’ rhetoric (Russell Neu- man et al., 2014). To shed light on this important topic, we focus on one issue—immigration—and develop a new methodology to computationally analyze its framing on Twitter. Our work highlights unique insights that social media data offers. The massive amount of available social media content enables us to compare framing strategies across countries and political ideologies. Furthermore, social media provides unique insights into how messages resonate with audiences through interactive signals such as retweets and favorites. By jointly analyzing the production and reception of frames on Twitter, we provide an in-depth analy- sis of immigration framing by and on the public. Political communications research has identi- fied numerous typologies of frames, such as issue- generic policy, immigration-specific, and narrative. Each of these frame types can significantly shape the audience’s perceptions of an issue (Iyengar, 1991; Chong and Druckman, 2007; Lecheler et al., 2015), but prior NLP work seeking to detect frames in mass media (e.g. Card et al., 2016; Field et al., 2018; Kwak et al., 2020) has largely been limited to a single issue-generic policy typology. Multi- ple dimensions of framing must be considered in order to better understand the structure of immi- gration discourse and its effect on public opinion and attitudes. We thus create a novel dataset …

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