# Retain or Reframe? A Computational Framework for the Analysis of Framing in News Articles and Reader Comments

arXiv: https://arxiv.org/abs/2507.04612 · HTML: https://mlsanity.com/paper/2507.04612/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 45 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [Neural Discourse Structure for Text Categorization](https://mlsanity.com/paper/1702.01829/) (2017)
- [Issue Framing in Online Discussion Fora](https://mlsanity.com/paper/1904.03969/) (2019)
- [Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media](https://mlsanity.com/paper/2205.12382/) (2022)
- [Multi-Modal Framing Analysis of News](https://mlsanity.com/paper/2503.20960/) (2025)
- [Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames](https://mlsanity.com/paper/2104.11030/) (2021)
- [Generalizability of Media Frames: Corpus creation and analysis across countries](https://mlsanity.com/paper/2506.16337/) (2025)

## Built on by (most cited first, 3 of 3)

- [Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles](https://mlsanity.com/paper/2510.03898/) (2025)
- [Article and Comment Frames Shape the Quality of Online Comments](https://mlsanity.com/paper/2603.27889/) (2026)
- [Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities](https://mlsanity.com/paper/2610.12610/) (2026)

## Abstract (arXiv)

When a news article describes immigration as an "economic burden" or a "humanitarian crisis," it selectively emphasizes certain aspects of the issue. Although textitframing shapes how the public interprets such issues, audiences do not absorb frames passively but actively reorganize the presented information. While this relationship between source content and audience response is well-documented in the social sciences, NLP approaches often ignore it, detecting frames in articles and responses in isolation. We present the first computational framework for large-scale analysis of framing across source content (news articles) and audience responses (reader comments). Methodologically, we refine frame labels and develop a framework that reconstructs dominant frames in articles and comments from sentence-level predictions, and aligns articles with topically relevant comments. Applying our framework across eleven topics and two news outlets, we find that frame reuse in comments correlates highly across outlets, while topic-specific patterns vary. We release a frame classifier that performs well on both articles and comments, a dataset of article and comment sentences manually labeled for frames, and a large-scale dataset of articles and comments with predicted frame labels.