# The Landscape of User-centered Misinformation Interventions -- A Systematic Literature Review

arXiv: https://arxiv.org/abs/2301.06517 · HTML: https://mlsanity.com/paper/2301.06517/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2023.

## Built on by (most cited first, 4 of 4)

- [Can Knowledge Editing Really Correct Hallucinations?](https://mlsanity.com/paper/2410.16251/) (2024)
- [Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media](https://mlsanity.com/paper/2503.05711/) (2025)
- [Don't be Fooled: The Misinformation Effect of Explanations in Human-AI Collaboration](https://mlsanity.com/paper/2409.12809/) (2024)
- [pytopicgram: A library for data extraction and topic modeling from Telegram channels](https://mlsanity.com/paper/2502.04882/) (2025)

## Abstract (arXiv)

Misinformation is one of the key challenges facing society today. User-centered misinformation interventions as digital countermeasures that exert a direct influence on users represent a promising means to deal with the large amounts of information available. While an extensive body of research on this topic exists, researchers are confronted with a diverse research landscape spanning multiple disciplines. This review systematizes the landscape of user-centered misinformation interventions to facilitate knowledge transfer, identify trends, and enable informed decision-making. Over 5,700 scholarly publications were screened and a systematic literature review (N=163) was conducted. A taxonomy was derived regarding intervention design (e.g., (binary) label), user interaction (active or passive), and timing (e.g., post exposure to misinformation). We provide a structured overview of approaches across multiple disciplines, and derive six overarching challenges for future research.