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What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020
Most studies analyzing political traffic on Social Networks focus on a single platform, while campaigns and reactions to political events produce interactions across different social media. Ignoring such cross-platform traffic may lead to analytical errors, missing important interactions across soci...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9888715/ https://www.ncbi.nlm.nih.gov/pubmed/36719868 http://dx.doi.org/10.1371/journal.pone.0270542 |
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author | Shevtsov, Alexander Oikonomidou, Maria Antonakaki, Despoina Pratikakis, Polyvios Ioannidis, Sotiris |
author_facet | Shevtsov, Alexander Oikonomidou, Maria Antonakaki, Despoina Pratikakis, Polyvios Ioannidis, Sotiris |
author_sort | Shevtsov, Alexander |
collection | PubMed |
description | Most studies analyzing political traffic on Social Networks focus on a single platform, while campaigns and reactions to political events produce interactions across different social media. Ignoring such cross-platform traffic may lead to analytical errors, missing important interactions across social media that e.g. explain the cause of trending or viral discussions. This work links Twitter and YouTube social networks using cross-postings of video URLs on Twitter to discover the main tendencies and preferences of the electorate, distinguish users and communities’ favouritism towards an ideology or candidate, study the sentiment towards candidates and political events, and measure political homophily. This study shows that Twitter communities correlate with YouTube comment communities: that is, Twitter users belonging to the same community in the Retweet graph tend to post YouTube video links with comments from YouTube users belonging to the same community in the YouTube Comment graph. Specifically, we identify Twitter and YouTube communities, we measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter. To achieve that, we have gather a dataset of approximately 20M tweets and the comments of 29K YouTube videos; we present the volume, the sentiment, and the communities formed in YouTube and Twitter graphs, and publish a representative sample of the dataset, as allowed by the corresponding Twitter policy restrictions. |
format | Online Article Text |
id | pubmed-9888715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-98887152023-02-01 What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 Shevtsov, Alexander Oikonomidou, Maria Antonakaki, Despoina Pratikakis, Polyvios Ioannidis, Sotiris PLoS One Research Article Most studies analyzing political traffic on Social Networks focus on a single platform, while campaigns and reactions to political events produce interactions across different social media. Ignoring such cross-platform traffic may lead to analytical errors, missing important interactions across social media that e.g. explain the cause of trending or viral discussions. This work links Twitter and YouTube social networks using cross-postings of video URLs on Twitter to discover the main tendencies and preferences of the electorate, distinguish users and communities’ favouritism towards an ideology or candidate, study the sentiment towards candidates and political events, and measure political homophily. This study shows that Twitter communities correlate with YouTube comment communities: that is, Twitter users belonging to the same community in the Retweet graph tend to post YouTube video links with comments from YouTube users belonging to the same community in the YouTube Comment graph. Specifically, we identify Twitter and YouTube communities, we measure their similarity and differences and show the interactions and the correlation between the largest communities on YouTube and Twitter. To achieve that, we have gather a dataset of approximately 20M tweets and the comments of 29K YouTube videos; we present the volume, the sentiment, and the communities formed in YouTube and Twitter graphs, and publish a representative sample of the dataset, as allowed by the corresponding Twitter policy restrictions. Public Library of Science 2023-01-31 /pmc/articles/PMC9888715/ /pubmed/36719868 http://dx.doi.org/10.1371/journal.pone.0270542 Text en © 2023 Shevtsov et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Shevtsov, Alexander Oikonomidou, Maria Antonakaki, Despoina Pratikakis, Polyvios Ioannidis, Sotiris What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 |
title | What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 |
title_full | What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 |
title_fullStr | What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 |
title_full_unstemmed | What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 |
title_short | What Tweets and YouTube comments have in common? Sentiment and graph analysis on data related to US elections 2020 |
title_sort | what tweets and youtube comments have in common? sentiment and graph analysis on data related to us elections 2020 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9888715/ https://www.ncbi.nlm.nih.gov/pubmed/36719868 http://dx.doi.org/10.1371/journal.pone.0270542 |
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