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Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19
Bursts and collective emotion have been widely studied in social physics field where researchers use mathematical models to understand human social dynamics. However, few researches recognize and separately analyze the internal and external influence on burst behaviors. To bridge this gap, we introd...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873493/ https://www.ncbi.nlm.nih.gov/pubmed/35210492 http://dx.doi.org/10.1038/s41598-022-07067-w |
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author | Wu, Qianyun Sano, Yukie Takayasu, Hideki Takayasu, Misako |
author_facet | Wu, Qianyun Sano, Yukie Takayasu, Hideki Takayasu, Misako |
author_sort | Wu, Qianyun |
collection | PubMed |
description | Bursts and collective emotion have been widely studied in social physics field where researchers use mathematical models to understand human social dynamics. However, few researches recognize and separately analyze the internal and external influence on burst behaviors. To bridge this gap, we introduce a non-parametric approach to classify an interevent time series into five scenarios: random arrival, endogenous burst, endogenous non-burst, exogenous burst and exogenous non-burst. In order to process large-scale social media data, we first segment the interevent time series into sections by detecting change points. Then we use the rule-based algorithm to classify the time series based on its distribution. To validate our model, we analyze 27.2 million COVID-19 related comments collected from Chinese social media between January to October 2020. We adopt the emotion category called Profile of Mood States which consists of six emotions: Anger, Depression, Fatigue, Vigor, Tension and Confusion. This enables us to compare the burst features of different collective emotions during the COVID-19 period. The burst detection and classification approach introduced in this paper can also be applied to analyzing other complex systems, including but not limited to social media, financial market and signal processing. |
format | Online Article Text |
id | pubmed-8873493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88734932022-02-25 Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19 Wu, Qianyun Sano, Yukie Takayasu, Hideki Takayasu, Misako Sci Rep Article Bursts and collective emotion have been widely studied in social physics field where researchers use mathematical models to understand human social dynamics. However, few researches recognize and separately analyze the internal and external influence on burst behaviors. To bridge this gap, we introduce a non-parametric approach to classify an interevent time series into five scenarios: random arrival, endogenous burst, endogenous non-burst, exogenous burst and exogenous non-burst. In order to process large-scale social media data, we first segment the interevent time series into sections by detecting change points. Then we use the rule-based algorithm to classify the time series based on its distribution. To validate our model, we analyze 27.2 million COVID-19 related comments collected from Chinese social media between January to October 2020. We adopt the emotion category called Profile of Mood States which consists of six emotions: Anger, Depression, Fatigue, Vigor, Tension and Confusion. This enables us to compare the burst features of different collective emotions during the COVID-19 period. The burst detection and classification approach introduced in this paper can also be applied to analyzing other complex systems, including but not limited to social media, financial market and signal processing. Nature Publishing Group UK 2022-02-24 /pmc/articles/PMC8873493/ /pubmed/35210492 http://dx.doi.org/10.1038/s41598-022-07067-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Wu, Qianyun Sano, Yukie Takayasu, Hideki Takayasu, Misako Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19 |
title | Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19 |
title_full | Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19 |
title_fullStr | Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19 |
title_full_unstemmed | Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19 |
title_short | Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19 |
title_sort | classification of endogenous and exogenous bursts in collective emotions based on weibo comments during covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8873493/ https://www.ncbi.nlm.nih.gov/pubmed/35210492 http://dx.doi.org/10.1038/s41598-022-07067-w |
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