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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...

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Autores principales: La Barbera, David, Roitero, Kevin, Demartini, Gianluca, Mizzaro, Stefano, Spina, Damiano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
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.
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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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