# Encounters with Visual Misinformation and Labels Across Platforms: An Interview and Diary Study to Inform Ecosystem Approaches to Misinformation Interventions

arXiv: https://arxiv.org/abs/2011.12758 · HTML: https://mlsanity.com/paper/2011.12758/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [Releasing Graph Neural Networks with Differential Privacy Guarantees](https://mlsanity.com/paper/2109.08907/) (2021)
- [The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media](https://mlsanity.com/paper/2102.06109/) (2021)
- [Value Alignment of Social Media Ranking Algorithms](https://mlsanity.com/paper/2509.14434/) (2025)
- [Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent](https://mlsanity.com/paper/2304.08759/) (2023)
- [Signals of Provenance: Practices & Challenges of Navigating Indicators in AI-Generated Media for Sighted and Blind Individuals](https://mlsanity.com/paper/2505.16057/) (2025)
- [Iffy-Or-Not: Extending the Web to Support the Critical Evaluation of Fallacious Texts](https://mlsanity.com/paper/2503.14412/) (2025)

## Abstract (arXiv)

Since 2016, the amount of academic research with the keyword "misinformation" has more than doubled [2]. This research often focuses on article headlines shown in artificial testing environments, yet misinformation largely spreads through images and video posts shared in highly-personalized platform contexts. A foundation of qualitative research is necessary to begin filling this gap to ensure platforms' visual misinformation interventions are aligned with users' needs and understanding of information in their personal contexts, across platforms. In two studies, we combined in-depth interviews (n=15) with diary and co-design methods (n=23) to investigate how a broad mix of Americans exposed to misinformation during COVID-19 understand their visual information environments, including encounters with interventions such as Facebook fact-checking labels. Analysis reveals a deep division in user attitudes about platform labeling interventions for visual information which are perceived by many as overly paternalistic, biased, and punitive. Alongside these findings, we discuss our methods as a model for continued independent qualitative research on cross-platform user experiences of misinformation that inform interventions.