# Issue Framing in Online Discussion Fora

arXiv: https://arxiv.org/abs/1904.03969 · HTML: https://mlsanity.com/paper/1904.03969/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2019.

## Built on by (most cited first, 10 of 10)

- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://mlsanity.com/paper/2306.02052/) (2023)
- [ENTRUST: Argument Reframing with Language Models and Entailment](https://mlsanity.com/paper/2103.06758/) (2021)
- [Us vs. Them: A Dataset of Populist Attitudes, News Bias and Emotions](https://mlsanity.com/paper/2101.11956/) (2021)
- [Multi-Modal Framing Analysis of News](https://mlsanity.com/paper/2503.20960/) (2025)
- [Connecting the Dots in News Analysis: Bridging the Cross-Disciplinary Disparities in Media Bias and Framing](https://mlsanity.com/paper/2309.08069/) (2023)
- [Controlled Neural Sentence-Level Reframing of News Articles](https://mlsanity.com/paper/2109.04957/) (2021)
- [Retain or Reframe? A Computational Framework for the Analysis of Framing in News Articles and Reader Comments](https://mlsanity.com/paper/2507.04612/) (2025)
- [Multi-Task Learning Improves Performance In Deep Argument Mining Models](https://mlsanity.com/paper/2307.01401/) (2023)
- [Framing Social Movements on Social Media: Unpacking Diagnostic, Prognostic, and Motivational Strategies](https://mlsanity.com/paper/2406.13820/) (2024)

## Abstract (arXiv)

In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.