# Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background

arXiv: https://arxiv.org/abs/2005.06915 · HTML: https://mlsanity.com/paper/2005.06915/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 9 of 9)

- [Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles](https://mlsanity.com/paper/2402.17478/) (2024)
- [Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?](https://mlsanity.com/paper/2208.09214/) (2022)
- [Fine-Grained Appropriate Reliance: Human-AI Collaboration with a Multi-Step Transparent Decision Workflow for Complex Task Decomposition](https://mlsanity.com/paper/2501.10909/) (2025)
- [Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2504.19940/) (2025)
- [Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs](https://mlsanity.com/paper/2404.12994/) (2024)
- [Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2501.18265/) (2025)
- [Region-enhanced Deep Graph Convolutional Networks for Rumor Detection](https://mlsanity.com/paper/2206.07665/) (2022)
- [Context Does Matter: Implications for Crowdsourced Evaluation Labels in Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2404.09980/) (2024)
- [Beyond Seeing Is Believing: On Crowdsourced Detection of Audiovisual Deepfakes](https://mlsanity.com/paper/2605.04797/) (2026)

## Abstract (arXiv)

Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true and false statements. Usually such judgments are made by experts, like journalists for political statements or medical doctors for medical statements. In this paper, we follow a different approach and rely on (non-expert) crowd workers. This of course leads to the following research question: Can crowdsourcing be reliably used to assess the truthfulness of information and to create large-scale labeled collections for information credibility systems? To address this issue, we present the results of an extensive study based on crowdsourcing: we collect thousands of truthfulness assessments over two datasets, and we compare expert judgments with crowd judgments, expressed on scales with various granularity levels. We also measure the political bias and the cognitive background of the workers, and quantify their effect on the reliability of the data provided by the crowd.