# Political Framing: US COVID19 Blame Game

arXiv: https://arxiv.org/abs/2007.09655 · HTML: https://mlsanity.com/paper/2007.09655/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision](https://mlsanity.com/paper/2210.10031/) (2022)
- [Characterizing Partisan Political Narrative Frameworks about COVID-19 on Twitter](https://mlsanity.com/paper/2103.06960/) (2021)
- [Framing Analysis of Health-Related Narratives: Conspiracy versus Mainstream Media](https://mlsanity.com/paper/2401.10030/) (2024)
- [Event-driven timeseries analysis and the comparison of public reactions on COVID-19](https://mlsanity.com/paper/2104.14777/) (2021)
- [mCPT at SemEval-2023 Task 3: Multilingual Label-Aware Contrastive Pre-Training of Transformers for Few- and Zero-shot Framing Detection](https://mlsanity.com/paper/2303.09901/) (2023)
- [MemeMind at ArAIEval Shared Task: Spotting Persuasive Spans in Arabic Text with Persuasion Techniques Identification](https://mlsanity.com/paper/2408.04540/) (2024)
- [Building Arabic NLP from the Ground Up: Twenty Years of Lessons, Failures, and Open Problems](https://mlsanity.com/paper/2605.20786/) (2026)

## Abstract (arXiv)

Through the use of Twitter, framing has become a prominent presidential campaign tool for politically active users. Framing is used to influence thoughts by evoking a particular perspective on an event. In this paper, we show that the COVID19 pandemic rather than being viewed as a public health issue, political rhetoric surrounding it is mostly shaped through a blame frame (blame Trump, China, or conspiracies) and a support frame (support candidates) backing the agenda of Republican and Democratic users in the lead up to the 2020 presidential campaign. We elucidate the divergences between supporters of both parties on Twitter via the use of frames. Additionally, we show how framing is used to positively or negatively reinforce users' thoughts. We look at how Twitter can efficiently be used to identify frames for topics through a reproducible pipeline.