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Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias
News content can sometimes be misleading and influence users’ decision making processes (e.g., voting decisions). Quantitatively assessing the truthfulness of content becomes key, but it is often challenging and thus done by experts. In this work we look at how experts and non-expert assess truthful...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148047/ http://dx.doi.org/10.1007/978-3-030-45442-5_26 |
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author | La Barbera, David Roitero, Kevin Demartini, Gianluca Mizzaro, Stefano Spina, Damiano |
author_facet | La Barbera, David Roitero, Kevin Demartini, Gianluca Mizzaro, Stefano Spina, Damiano |
author_sort | La Barbera, David |
collection | PubMed |
description | News content can sometimes be misleading and influence users’ decision making processes (e.g., voting decisions). Quantitatively assessing the truthfulness of content becomes key, but it is often challenging and thus done by experts. In this work we look at how experts and non-expert assess truthfulness of content by focusing on the effect of the adopted judgment scale and of assessors’ own bias on the judgments they perform. Our results indicate a clear effect of the assessors’ political background on their judgments where they tend to trust content which is aligned to their own belief, even if experts have marked it as false. Crowd assessors also seem to have a preference towards coarse-grained scales, as they tend to use a few extreme values rather than the full breadth of fine-grained scales. |
format | Online Article Text |
id | pubmed-7148047 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-71480472020-04-13 Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias La Barbera, David Roitero, Kevin Demartini, Gianluca Mizzaro, Stefano Spina, Damiano Advances in Information Retrieval Article News content can sometimes be misleading and influence users’ decision making processes (e.g., voting decisions). Quantitatively assessing the truthfulness of content becomes key, but it is often challenging and thus done by experts. In this work we look at how experts and non-expert assess truthfulness of content by focusing on the effect of the adopted judgment scale and of assessors’ own bias on the judgments they perform. Our results indicate a clear effect of the assessors’ political background on their judgments where they tend to trust content which is aligned to their own belief, even if experts have marked it as false. Crowd assessors also seem to have a preference towards coarse-grained scales, as they tend to use a few extreme values rather than the full breadth of fine-grained scales. 2020-03-24 /pmc/articles/PMC7148047/ http://dx.doi.org/10.1007/978-3-030-45442-5_26 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article La Barbera, David Roitero, Kevin Demartini, Gianluca Mizzaro, Stefano Spina, Damiano Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias |
title | Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias |
title_full | Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias |
title_fullStr | Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias |
title_full_unstemmed | Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias |
title_short | Crowdsourcing Truthfulness: The Impact of Judgment Scale and Assessor Bias |
title_sort | crowdsourcing truthfulness: the impact of judgment scale and assessor bias |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148047/ http://dx.doi.org/10.1007/978-3-030-45442-5_26 |
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