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Emotions explain differences in the diffusion of true vs. false social media rumors

False rumors (often termed “fake news”) on social media pose a significant threat to modern societies. However, potential reasons for the widespread diffusion of false rumors have been underexplored. In this work, we analyze whether sentiment words, as well as different emotional words, in social me...

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Autores principales: Pröllochs, Nicolas, Bär, Dominik, Feuerriegel, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608927/
https://www.ncbi.nlm.nih.gov/pubmed/34811397
http://dx.doi.org/10.1038/s41598-021-01813-2
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author Pröllochs, Nicolas
Bär, Dominik
Feuerriegel, Stefan
author_facet Pröllochs, Nicolas
Bär, Dominik
Feuerriegel, Stefan
author_sort Pröllochs, Nicolas
collection PubMed
description False rumors (often termed “fake news”) on social media pose a significant threat to modern societies. However, potential reasons for the widespread diffusion of false rumors have been underexplored. In this work, we analyze whether sentiment words, as well as different emotional words, in social media content explain differences in the spread of true vs. false rumors. For this purpose, we collected [Formula: see text] rumor cascades from Twitter, comprising more than 4.5 million retweets that have been fact-checked for veracity. We then categorized the language in social media content to (1) sentiment (i.e., positive vs. negative) and (2) eight basic emotions (i. e., anger, anticipation, disgust, fear, joy, trust, sadness, and surprise). We find that sentiment and basic emotions explain differences in the structural properties of true vs. false rumor cascades. False rumors (as compared to true rumors) are more likely to go viral if they convey a higher proportion of terms associated with a positive sentiment. Further, false rumors are viral when embedding emotional words classified as trust, anticipation, or anger. All else being equal, false rumors conveying one standard deviation more positive sentiment have a 37.58% longer lifetime and reach 61.44% more users. Our findings offer insights into how true vs. false rumors spread and highlight the importance of managing emotions in social media content.
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spelling pubmed-86089272021-11-24 Emotions explain differences in the diffusion of true vs. false social media rumors Pröllochs, Nicolas Bär, Dominik Feuerriegel, Stefan Sci Rep Article False rumors (often termed “fake news”) on social media pose a significant threat to modern societies. However, potential reasons for the widespread diffusion of false rumors have been underexplored. In this work, we analyze whether sentiment words, as well as different emotional words, in social media content explain differences in the spread of true vs. false rumors. For this purpose, we collected [Formula: see text] rumor cascades from Twitter, comprising more than 4.5 million retweets that have been fact-checked for veracity. We then categorized the language in social media content to (1) sentiment (i.e., positive vs. negative) and (2) eight basic emotions (i. e., anger, anticipation, disgust, fear, joy, trust, sadness, and surprise). We find that sentiment and basic emotions explain differences in the structural properties of true vs. false rumor cascades. False rumors (as compared to true rumors) are more likely to go viral if they convey a higher proportion of terms associated with a positive sentiment. Further, false rumors are viral when embedding emotional words classified as trust, anticipation, or anger. All else being equal, false rumors conveying one standard deviation more positive sentiment have a 37.58% longer lifetime and reach 61.44% more users. Our findings offer insights into how true vs. false rumors spread and highlight the importance of managing emotions in social media content. Nature Publishing Group UK 2021-11-22 /pmc/articles/PMC8608927/ /pubmed/34811397 http://dx.doi.org/10.1038/s41598-021-01813-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Pröllochs, Nicolas
Bär, Dominik
Feuerriegel, Stefan
Emotions explain differences in the diffusion of true vs. false social media rumors
title Emotions explain differences in the diffusion of true vs. false social media rumors
title_full Emotions explain differences in the diffusion of true vs. false social media rumors
title_fullStr Emotions explain differences in the diffusion of true vs. false social media rumors
title_full_unstemmed Emotions explain differences in the diffusion of true vs. false social media rumors
title_short Emotions explain differences in the diffusion of true vs. false social media rumors
title_sort emotions explain differences in the diffusion of true vs. false social media rumors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608927/
https://www.ncbi.nlm.nih.gov/pubmed/34811397
http://dx.doi.org/10.1038/s41598-021-01813-2
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