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Modeling Framing in Immigration Discourse on Social Media

Paper · 2021 · Julia Mendelsohn, Ceren Budak and David Jurgens

Summary

Most computational framing research examines news outlets and political elites and uses a single issue-generic policy typology. This paper asks how ordinary Twitter users frame immigration. The authors built a new corpus of immigration-related tweets annotated with three complementary typologies from political communication theory:

  • issue-generic policy frames (Boydstun et al.)
  • immigration-specific frames, which portray immigrants as heroes, victims or threats (Benson)
  • episodic vs. thematic narrative frames (Iyengar)

They trained and compared several supervised frame detectors and applied them at scale. They then examined two questions:

  • Frame-building: how author ideology and country (US, UK, EU) shape framing.
  • Frame-setting: how framing affects favorites and retweets.

Immigration-specific frames revealed ideological and regional differences that generic frames concealed. They also had a stronger effect on engagement than generic frames. Human-interest, cultural and political frames drew more engagement. The work shows that multi-typology framing analysis matters for both NLP and social science.

Key points

  • Annotation scheme: 27 frames in total, comprising 14 issue-generic policy frames, 11 immigration-specific frames (4 victim, 3 hero, 4 threat) and 2 narrative frames (episodic, thematic).
  • Regional differences: Crime & punishment, security & defense, threat: public order and threat: fiscal appear significantly more often in US tweets than in UK or EU tweets. Region effects were tested with Holm-Bonferroni correction.
  • Engagement models: Hierarchical linear mixed-effects models predicted log-transformed favorites and retweets. They controlled for temporal, user-level and tweet-level features, as well as ideology.
  • Engagement findings:
    • Cultural and human-interest frames (e.g., hero: integration, morality, victim: discrimination) are linked to more favorites.
    • Political factors & implications and security/safety frames are linked to more retweets.
    • Both narrative frames increase retweets, but less strongly than issue frames.
  • Resources: Code, data, annotation guidelines and pretrained models are released at github.com/juliamendelsohn/framing. The provided excerpt omits the middle of the paper, so this summary cannot report dataset size or classifier scores.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of MichiganAnn Arbor · United States36 works · 12,181 citations

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksSocial Sciences · 744 worksGeneral Social Sciences · 87 worksCommunication · 44 works

🧭 Topics 1Research subjects and languages the work is about.Discourse analysis · 394 works

Cite

Cite as Julia Mendelsohn, Ceren Budak and David Jurgens. 2021. 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.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2021.naacl-main.179

@inproceedings{mendelsohn2021modeling,
  title = {{Modeling Framing in Immigration Discourse on Social Media}},
  author = {Julia Mendelsohn, Ceren Budak and David Jurgens},
  booktitle = {Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
  year = {2021},
  doi = {10.18653/v1/2021.naacl-main.179},
  url = {https://doi.org/10.18653/v1/2021.naacl-main.179}
}

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