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

Abstract

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.

References in corpus (12)

• Fake News Detection on Social Media: A Data Mining Perspective
• NudgeCred: Supporting News Credibility Assessment on Social Media Through Nudges
• Understanding Effects of Algorithmic vs. Community Label on Perceived Accuracy of Hyper-partisan Misinformation
• Designing Transparency Cues in Online News Platforms to Promote Trust: Journalists' & Consumers' Perspectives
• A Comparative Evaluation of Interventions Against Misinformation: Augmenting the WHO Checklist
• A Browser Extension for in-place Signaling and Assessment of Misinformation
• Remove, Reduce, Inform: What Actions do People Want Social Media Platforms to Take on Potentially Misleading Content?
• Ask to Know More: Generating Counterfactual Explanations for Fake Claims
• Effects of Automated Misinformation Warning Labels on the Intents to Like, Comment and Share Posts
• RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media
• Exoskeleton for the Mind: Exploring Strategies Against Misinformation with a Metacognitive Agent
• Visual Selective Attention System to Intervene User Attention in Sharing COVID-19 Misinformation

Cited by in corpus (2)

• Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media
• pytopicgram: A library for data extraction and topic modeling from Telegram channels