Name
Title | ||
|---|---|---|
Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background |
Abstract | ||
|---|---|---|
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.
|
Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3397271.3401112 | SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval
Virtual Event
China
July, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-8016-4 | 0 |
PageRank | References | Authors |
0.34 | 32 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Roitero | 1 | 30 | 13.74 |
| Michael Soprano | 2 | 1 | 2.72 |
| Fan Shaoyang | 3 | 0 | 0.34 |
| Damiano Spina | 4 | 347 | 29.96 |
| Stefano Mizzaro | 5 | 862 | 85.52 |
| Gianluca Demartini | 6 | 744 | 54.56 |