# Modeling Framing in Immigration Discourse on Social Media

arXiv: https://arxiv.org/abs/2104.06443 · HTML: https://mlsanity.com/paper/2104.06443/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 74 references that are in the map.
- First posted on arXiv 13 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017](https://mlsanity.com/paper/2005.01803/) (2020)
- [Issue Framing in Online Discussion Fora](https://mlsanity.com/paper/1904.03969/) (2019)
- [Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization](https://mlsanity.com/paper/1404.7152/) (2014)
- [S'i o no, qu`e penses? Catalonian Independence and Linguistic Identity on Social Media](https://mlsanity.com/paper/1804.05088/) (2018)
- [Ratioing the President: An exploration of public engagement with Obama and Trump on Twitter](https://mlsanity.com/paper/2006.03526/) (2020)

## Built on by (most cited first, 30 of 37)

- [POTATO: The Portable Text Annotation Tool](https://mlsanity.com/paper/2212.08620/) (2022)
- [DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detection](https://mlsanity.com/paper/2402.10426/) (2024)
- [Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections](https://mlsanity.com/paper/2305.05094/) (2023)
- [Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Models](https://mlsanity.com/paper/2503.16148/) (2025)
- [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://mlsanity.com/paper/2306.02052/) (2023)
- [To Protect and To Serve? Analyzing Entity-Centric Framing of Police Violence](https://mlsanity.com/paper/2109.05325/) (2021)
- [Automatic Identification and Classification of Bragging in Social Media](https://mlsanity.com/paper/2203.05840/) (2022)
- [Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media](https://mlsanity.com/paper/2205.12382/) (2022)
- [Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity](https://mlsanity.com/paper/2104.08829/) (2021)
- [Multi-Modal Framing Analysis of News](https://mlsanity.com/paper/2503.20960/) (2025)
- [OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants](https://mlsanity.com/paper/2406.14883/) (2024)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection](https://mlsanity.com/paper/2402.11621/) (2024)
- [Media Framing through the Lens of Event-Centric Narratives](https://mlsanity.com/paper/2410.03151/) (2024)
- [When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models](https://mlsanity.com/paper/2502.13246/) (2025)
- [Detecting Frames in News Headlines and Lead Images in U.S. Gun Violence Coverage](https://mlsanity.com/paper/2406.17213/) (2024)
- [Talking Point based Ideological Discourse Analysis in News Events](https://mlsanity.com/paper/2504.07400/) (2025)
- [Generalizability of Media Frames: Corpus creation and analysis across countries](https://mlsanity.com/paper/2506.16337/) (2025)
- [Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models](https://mlsanity.com/paper/2408.07873/) (2024)
- [What Media Frames Reveal About Stance: A Dataset and Study about Memes in Climate Change Discourse](https://mlsanity.com/paper/2505.16592/) (2025)
- [Retain or Reframe? A Computational Framework for the Analysis of Framing in News Articles and Reader Comments](https://mlsanity.com/paper/2507.04612/) (2025)
- [Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News](https://mlsanity.com/paper/2402.14224/) (2024)
- [Modeling Empathetic Alignment in Conversation](https://mlsanity.com/paper/2405.00948/) (2024)
- [Unsupervised Detection of Contextualized Embedding Bias with Application to Ideology](https://mlsanity.com/paper/2212.07547/) (2022)
- [Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets](https://mlsanity.com/paper/2309.09508/) (2023)
- [A Structured Clustering Approach for Inducing Media Narratives](https://mlsanity.com/paper/2604.10368/) (2026)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)
- [Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis](https://mlsanity.com/paper/2211.02269/) (2022)
- [A Study on Scaling Up Multilingual News Framing Analysis](https://mlsanity.com/paper/2404.01481/) (2024)
- [Effects of Collaboration on the Performance of Interactive Theme Discovery Systems](https://mlsanity.com/paper/2408.09030/) (2024)

## Abstract (arXiv)

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.