A Survey on the Role of Crowds in Combating Online Misinformation: Annotators, Evaluators, and Creators
Not on the map. Why?
Abstract (arXiv)
Online misinformation poses a global risk with significant real-world consequences. To combat misinformation, current research relies on professionals like journalists and fact-checkers for annotating and debunking misinformation, and develops automated machine learning methods for detecting misinformation. Complementary to these approaches, recent research has increasingly concentrated on utilizing the power of ordinary social media users, a.k.a. "crowd", who act as eyes-on-the-ground proactively questioning and countering misinformation. Notably, recent studies show that 96% of counter-misinformation responses originate from them. Acknowledging their prominent role, we present the first systematic and comprehensive survey of research papers that actively leverage the crowds to combat misinformation. We first identify 88 papers related to crowd-based efforts, following a meticulous annotation process adhering to the PRISMA framework. We then present key statistics related to misinformation, counter-misinformation, and crowd input in different formats and topics. Upon holistic analysis of the papers, we introduce a novel taxonomy of the roles played by the crowds: (i)annotators who actively identify misinformation; (ii)evaluators who assess counter-misinformation effectiveness; (iii)creators who create counter-misinformation. This taxonomy explores the crowd's capabilities in misinformation detection, identifies prerequisites for effective counter-misinformation, and analyzes crowd-generated counter-misinformation. Then, we delve into (i)distinguishing individual, collaborative, and machine-assisted labeling for annotators; (ii)analyzing the effectiveness of counter-misinformation through surveys, interviews, and in-lab experiments for evaluators; and (iii)characterizing creation patterns and creator profiles for creators. Finally, we outline potential future research in this field.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
- 2024MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platformscited by 26
- 2024NLP for Counterspeech against Hate: A Survey and How-To Guidecited by 21
- 2025Assessing the Potential of Generative Agents in Crowdsourced Fact-Checkingcited by 3
- 2026Optimal Transport for LLM Reward Modeling from Noisy Preferencecited by 3
- 2025The Influence of Text Variation on User Engagement in Cross-Platform Content Sharingcited by 0
- 2026Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakescited by 0
- 2026Optimal Transport for LLM Reward Modeling from Noisy Preference7 May 2026
- 2026Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes6 May 2026
- 2025The Influence of Text Variation on User Engagement in Cross-Platform Content Sharing26 Apr 2025
- 2025Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking24 Apr 2025
- 2024NLP for Counterspeech against Hate: A Survey and How-To Guide29 Mar 2024
- 2024MM-Soc: Benchmarking Multimodal Large Language Models in Social Media Platforms21 Feb 2024