In Crowd Veritas: Leveraging Human Intelligence To Fight Misinformation

TL;DR

This paper explores how crowdsourcing and human judgment can be effectively utilized to evaluate online misinformation, addressing challenges of scale, bias, and interpretability in fact-checking.

Contribution

It introduces a statistical model for predicting and explaining truthfulness based on large-scale crowdsourcing data, enhancing transparency and trustworthiness.

Findings

Non-expert judgments often align with expert assessments.

Timing and experience influence judgment accuracy.

The proposed model improves interpretability of misinformation assessment.

Abstract

The spread of online misinformation poses serious threats to democratic societies. Traditionally, expert fact-checkers verify the truthfulness of information through investigative processes. However, the volume and immediacy of online content present major scalability challenges. Crowdsourcing offers a promising alternative by leveraging non-expert judgments, but it introduces concerns about bias, accuracy, and interpretability. This thesis investigates how human intelligence can be harnessed to assess the truthfulness of online information, focusing on three areas: misinformation assessment, cognitive biases, and automated fact-checking systems. Through large-scale crowdsourcing experiments and statistical modeling, it identifies key factors influencing human judgments and introduces a model for the joint prediction and explanation of truthfulness. The findings show that non-expert judgments often align with expert assessments, particularly when factors such as timing and experience are considered. By deepening our understanding of human judgment and bias in truthfulness assessment, this thesis contributes to the development of more transparent, trustworthy, and interpretable systems for combating misinformation.