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Evolution of topics and hate speech in retweet network communities
Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686097/ https://www.ncbi.nlm.nih.gov/pubmed/34957317 http://dx.doi.org/10.1007/s41109-021-00439-7 |
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author | Evkoski, Bojan Ljubešić, Nikola Pelicon, Andraž Mozetič, Igor Kralj Novak, Petra |
author_facet | Evkoski, Bojan Ljubešić, Nikola Pelicon, Andraž Mozetič, Igor Kralj Novak, Petra |
author_sort | Evkoski, Bojan |
collection | PubMed |
description | Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions: temporal evolution of retweet networks and communities, contents in terms of hate speech, and discussion topics. We apply the methods to a comprehensive set of all Slovenian tweets collected in the years 2018–2020. We find that politics and ideology are the prevailing topics despite the emergence of the Covid-19 pandemic. These two topics also attract the highest proportion of unacceptable tweets. Through time, the membership of retweet communities changes, but their topic distribution remains remarkably stable. Some retweet communities are strongly linked by external retweet influence and form super-communities. The super-community membership closely corresponds to the topic distribution: communities from the same super-community are very similar by the topic distribution, and communities from different super-communities are quite different in terms of discussion topics. However, we also find that even communities from the same super-community differ considerably in the proportion of unacceptable tweets they post. |
format | Online Article Text |
id | pubmed-8686097 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-86860972021-12-20 Evolution of topics and hate speech in retweet network communities Evkoski, Bojan Ljubešić, Nikola Pelicon, Andraž Mozetič, Igor Kralj Novak, Petra Appl Netw Sci Research Twitter data exhibits several dimensions worth exploring: a network dimension in the form of links between the users, textual content of the tweets posted, and a temporal dimension as the time-stamped sequence of tweets and their retweets. In the paper, we combine analyses along all three dimensions: temporal evolution of retweet networks and communities, contents in terms of hate speech, and discussion topics. We apply the methods to a comprehensive set of all Slovenian tweets collected in the years 2018–2020. We find that politics and ideology are the prevailing topics despite the emergence of the Covid-19 pandemic. These two topics also attract the highest proportion of unacceptable tweets. Through time, the membership of retweet communities changes, but their topic distribution remains remarkably stable. Some retweet communities are strongly linked by external retweet influence and form super-communities. The super-community membership closely corresponds to the topic distribution: communities from the same super-community are very similar by the topic distribution, and communities from different super-communities are quite different in terms of discussion topics. However, we also find that even communities from the same super-community differ considerably in the proportion of unacceptable tweets they post. Springer International Publishing 2021-12-20 2021 /pmc/articles/PMC8686097/ /pubmed/34957317 http://dx.doi.org/10.1007/s41109-021-00439-7 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 | Research Evkoski, Bojan Ljubešić, Nikola Pelicon, Andraž Mozetič, Igor Kralj Novak, Petra Evolution of topics and hate speech in retweet network communities |
title | Evolution of topics and hate speech in retweet network communities |
title_full | Evolution of topics and hate speech in retweet network communities |
title_fullStr | Evolution of topics and hate speech in retweet network communities |
title_full_unstemmed | Evolution of topics and hate speech in retweet network communities |
title_short | Evolution of topics and hate speech in retweet network communities |
title_sort | evolution of topics and hate speech in retweet network communities |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8686097/ https://www.ncbi.nlm.nih.gov/pubmed/34957317 http://dx.doi.org/10.1007/s41109-021-00439-7 |
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