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Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach
The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871728/ https://www.ncbi.nlm.nih.gov/pubmed/35206813 http://dx.doi.org/10.3390/healthcare10020198 |
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author | Xu, Heng Zeng, Jun Tai, Zhaodan Hao, Huihui |
author_facet | Xu, Heng Zeng, Jun Tai, Zhaodan Hao, Huihui |
author_sort | Xu, Heng |
collection | PubMed |
description | The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour. |
format | Online Article Text |
id | pubmed-8871728 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-88717282022-02-25 Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach Xu, Heng Zeng, Jun Tai, Zhaodan Hao, Huihui Healthcare (Basel) Article The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour. MDPI 2022-01-20 /pmc/articles/PMC8871728/ /pubmed/35206813 http://dx.doi.org/10.3390/healthcare10020198 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Xu, Heng Zeng, Jun Tai, Zhaodan Hao, Huihui Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach |
title | Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach |
title_full | Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach |
title_fullStr | Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach |
title_full_unstemmed | Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach |
title_short | Public Attention and Sentiment toward Intimate Partner Violence Based on Weibo in China: A Text Mining Approach |
title_sort | public attention and sentiment toward intimate partner violence based on weibo in china: a text mining approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871728/ https://www.ncbi.nlm.nih.gov/pubmed/35206813 http://dx.doi.org/10.3390/healthcare10020198 |
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