Can The Crowd Identify Misinformation Objectively? The Effects of Judgments Scale and Assessor's Bias

  • Kevin Roitero
  • Michael Soprano
  • Shaoyang Fan
  • Damiano Spina
  • Stefano Mizzaro
  • Gianluca Demartini
Proceedings of SIGIR'20, 2020

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 labelled 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.

    author = {Roitero, Kevin and Soprano, Michael  and Fan, Shaoyang  and  
                     Spina, Damiano and Mizzaro, Stefano  and Demartini, Gianluca},
title = {Can The Crowd Identify Misinformation Objectively? 
            The Effects of Judgment Scale and Assessor's Background},
booktitle= {Proceedings of SIGIR'20},
year = {2020}
Damiano Spina