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Analyzing voter behavior on social media during the 2020 US presidential election campaign

Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential elect...

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Autores principales: Belcastro, Loris, Branda, Francesco, Cantini, Riccardo, Marozzo, Fabrizio, Talia, Domenico, Trunfio, Paolo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288921/
https://www.ncbi.nlm.nih.gov/pubmed/35873661
http://dx.doi.org/10.1007/s13278-022-00913-9
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author Belcastro, Loris
Branda, Francesco
Cantini, Riccardo
Marozzo, Fabrizio
Talia, Domenico
Trunfio, Paolo
author_facet Belcastro, Loris
Branda, Francesco
Cantini, Riccardo
Marozzo, Fabrizio
Talia, Domenico
Trunfio, Paolo
author_sort Belcastro, Loris
collection PubMed
description Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.
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spelling pubmed-92889212022-07-18 Analyzing voter behavior on social media during the 2020 US presidential election campaign Belcastro, Loris Branda, Francesco Cantini, Riccardo Marozzo, Fabrizio Talia, Domenico Trunfio, Paolo Soc Netw Anal Min Original Article Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint. Springer Vienna 2022-07-18 2022 /pmc/articles/PMC9288921/ /pubmed/35873661 http://dx.doi.org/10.1007/s13278-022-00913-9 Text en © The Author(s) 2022 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 Original Article
Belcastro, Loris
Branda, Francesco
Cantini, Riccardo
Marozzo, Fabrizio
Talia, Domenico
Trunfio, Paolo
Analyzing voter behavior on social media during the 2020 US presidential election campaign
title Analyzing voter behavior on social media during the 2020 US presidential election campaign
title_full Analyzing voter behavior on social media during the 2020 US presidential election campaign
title_fullStr Analyzing voter behavior on social media during the 2020 US presidential election campaign
title_full_unstemmed Analyzing voter behavior on social media during the 2020 US presidential election campaign
title_short Analyzing voter behavior on social media during the 2020 US presidential election campaign
title_sort analyzing voter behavior on social media during the 2020 us presidential election campaign
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288921/
https://www.ncbi.nlm.nih.gov/pubmed/35873661
http://dx.doi.org/10.1007/s13278-022-00913-9
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