• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

《理智与情感:关于社交媒体用户使用新冠疫情争议性词汇的特征分析》

Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19.

作者信息

Lyu Hanjia, Chen Long, Wang Yu, Luo Jiebo

机构信息

Goergen Institute for Data ScienceUniversity of Rochester Rochester NY 14627 USA.

Department of Computer ScienceUniversity of Rochester Rochester NY 14627 USA.

出版信息

IEEE Trans Big Data. 2020 May 21;7(6):952-960. doi: 10.1109/TBDATA.2020.2996401. eCollection 2021 Dec.

DOI:10.1109/TBDATA.2020.2996401
PMID:35582463
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8851431/
Abstract

With the world-wide development of 2019 novel coronavirus, although WHO has officially announced the disease as COVID-19, one controversial term - "Chinese Virus" is still being used by a great number of people. In the meantime, global online media coverage about COVID-19-related racial attacks increases steadily, most of which are anti-Chinese or anti-Asian. As this pandemic becomes increasingly severe, more people start to talk about it on social media platforms such as Twitter. When they refer to COVID-19, there are mainly two ways: using controversial terms like "Chinese Virus" or "Wuhan Virus", or using non-controversial terms like "Coronavirus". In this article, we attempt to characterize the Twitter users who use controversial terms and those who use non-controversial terms. We use the Tweepy API to retrieve 17 million related tweets and the information of their authors. We find the significant differences between these two groups of Twitter users across their demographics, user-level features like the number of followers, political following status, as well as their geo-locations. Moreover, we apply classification models to predict Twitter users who are more likely to use controversial terms. To our best knowledge, this is the first large-scale social media-based study to characterize users with respect to their usage of controversial terms during a major crisis.

摘要

随着2019新型冠状病毒在全球范围内的传播,尽管世界卫生组织已正式将该疾病命名为COVID-19,但一个有争议的词汇——“中国病毒”仍被许多人使用。与此同时,全球在线媒体对与COVID-19相关的种族攻击的报道稳步增加,其中大部分是反华或反亚裔的。随着这场大流行病日益严重,越来越多的人开始在推特等社交媒体平台上谈论它。当他们提及COVID-19时,主要有两种方式:使用有争议的词汇,如“中国病毒”或“武汉病毒”,或使用无争议的词汇,如“冠状病毒”。在本文中,我们试图描述使用有争议词汇的推特用户和使用无争议词汇的推特用户的特征。我们使用Tweepy应用程序编程接口来检索1700万条相关推文及其作者的信息。我们发现这两组推特用户在人口统计学特征、用户层面的特征(如关注者数量、政治关注状态)以及地理位置方面存在显著差异。此外,我们应用分类模型来预测更有可能使用有争议词汇的推特用户。据我们所知,这是第一项基于大规模社交媒体的研究,旨在描述在重大危机期间使用有争议词汇的用户特征。

相似文献

1
Sense and Sensibility: Characterizing Social Media Users Regarding the Use of Controversial Terms for COVID-19.《理智与情感:关于社交媒体用户使用新冠疫情争议性词汇的特征分析》
IEEE Trans Big Data. 2020 May 21;7(6):952-960. doi: 10.1109/TBDATA.2020.2996401. eCollection 2021 Dec.
2
Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study.新冠疫情期间推特用户的主要担忧:信息监测研究
J Med Internet Res. 2020 Apr 21;22(4):e19016. doi: 10.2196/19016.
3
Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19-Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study.欧洲SARS-CoV-2疫情期间推特上新冠疫情相关信息传播的时间和地点变化以及链接类别:信息监测研究
J Med Internet Res. 2020 Aug 28;22(8):e19629. doi: 10.2196/19629.
4
Use of Twitter social media activity as a proxy for human mobility to predict the spatiotemporal spread of COVID-19 at global scale.利用推特社交媒体活动作为人类流动性的代理指标,以预测新冠病毒在全球范围内的时空传播。
Geospat Health. 2020 Jun 15;15(1). doi: 10.4081/gh.2020.882.
5
COVID-19 Vaccine Hesitancy on Social Media: Building a Public Twitter Data Set of Antivaccine Content, Vaccine Misinformation, and Conspiracies.社交媒体上对 COVID-19 疫苗的犹豫:构建一个关于反疫苗内容、疫苗错误信息和阴谋论的公共 Twitter 数据集。
JMIR Public Health Surveill. 2021 Nov 17;7(11):e30642. doi: 10.2196/30642.
6
Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea.推特上的对话与医学新闻框架:韩国新冠肺炎信息流行病学研究
J Med Internet Res. 2020 May 5;22(5):e18897. doi: 10.2196/18897.
7
The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets.新冠疫情期间网络种族主义的死灰复燃及其余波:推特中情绪的分析
JMIR Public Health Surveill. 2020 Oct 15;6(4):e19833. doi: 10.2196/19833.
8
Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set.追踪社交媒体上关于 COVID-19 大流行的讨论:公共冠状病毒 Twitter 数据集的开发。
JMIR Public Health Surveill. 2020 May 29;6(2):e19273. doi: 10.2196/19273.
9
Smoking, Vaping, and Tobacco Industry During COVID-19 Pandemic: Twitter Data Analysis.吸烟、蒸气吸入和 COVID-19 大流行期间的烟草业:推特数据分析。
Cyberpsychol Behav Soc Netw. 2020 Dec;23(12):811-817. doi: 10.1089/cyber.2020.0384. Epub 2020 Jul 30.
10
Creating COVID-19 Stigma by Referencing the Novel Coronavirus as the "Chinese virus" on Twitter: Quantitative Analysis of Social Media Data.在推特上将新型冠状病毒称为“中国病毒”从而制造新冠病毒污名化:社交媒体数据的定量分析
J Med Internet Res. 2020 May 6;22(5):e19301. doi: 10.2196/19301.

引用本文的文献

1
Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two U.S. States.分析新冠疫情大流行认知期内推特上的推文模式及公众参与度:对美国两个州的研究
IEEE Access. 2022;10:72879-72894. doi: 10.1109/access.2022.3189670.
2
Prevalence and Predictors of Posting Health-Related Content Among US Facebook Users: A Cross-Sectional Survey Study.美国脸书用户发布健康相关内容的患病率及预测因素:一项横断面调查研究。
Int J Environ Res Public Health. 2025 Jun 10;22(6):918. doi: 10.3390/ijerph22060918.
3
Misinformation about the COVID-19 Vaccine in Online Catholic Media.

本文引用的文献

1
High population densities catalyse the spread of COVID-19.高人口密度加速了新冠病毒的传播。
J Travel Med. 2020 May 18;27(3). doi: 10.1093/jtm/taaa038.
2
Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science.迈向第二代“社交媒体指标”:刻画科学关注的 Twitter 社区。
PLoS One. 2019 May 22;14(5):e0216408. doi: 10.1371/journal.pone.0216408. eCollection 2019.
3
Characterizing JUUL-related posts on Twitter.分析推特上与 JUUL 相关的帖子。
在线天主教媒体中关于新冠疫苗的错误信息。
Vaccines (Basel). 2023 Jun 1;11(6):1054. doi: 10.3390/vaccines11061054.
4
"Do as I say but not as I do": Influence of political leaders' populist communication styles on public adherence in a crisis using the global case of COVID-19 movement restrictions.“照我说的做,别照我做的做”:以新冠疫情行动限制这一全球案例探讨政治领导人的民粹主义沟通风格对危机期间公众遵守情况的影响
Data Inf Manag. 2023 Jun;7(2):100039. doi: 10.1016/j.dim.2023.100039. Epub 2023 Apr 20.
5
Human behavior in the time of COVID-19: Learning from big data.新冠疫情期间的人类行为:从大数据中学习
Front Big Data. 2023 Apr 6;6:1099182. doi: 10.3389/fdata.2023.1099182. eCollection 2023.
6
The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States.美国推特上的新冠疫情与心理健康问题
Health Data Sci. 2022 Feb 17;2022:9758408. doi: 10.34133/2022/9758408. eCollection 2022.
7
Misinformation versus Facts: Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake.错误信息与事实:了解有关新冠疫苗的新闻对疫苗接种率的影响。
Health Data Sci. 2022 Mar 12;2022:9858292. doi: 10.34133/2022/9858292. eCollection 2022.
8
How racial animus forms and spreads: Evidence from the coronavirus pandemic.种族敌意如何形成与传播:来自新冠疫情的证据。
J Econ Behav Organ. 2022 Aug;200:82-98. doi: 10.1016/j.jebo.2022.05.014. Epub 2022 May 27.
9
Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination.关于潜在新冠疫苗公众意见的社交媒体研究:揭示异议、差异及传播情况
Intell Med. 2022 Feb;2(1):1-12. doi: 10.1016/j.imed.2021.08.001. Epub 2021 Aug 25.
10
A Study of the Correlation between the Dates of the First Covid Case and the First Covid Death of 25 Selected Countries to know the Virulence of the Covid-19 in Different Tropical Conditions.一项关于25个选定国家首例新冠病例日期与首例新冠死亡日期之间相关性的研究,以了解新冠病毒在不同热带条件下的毒力。
Ethics Med Public Health. 2021 Dec;19:100707. doi: 10.1016/j.jemep.2021.100707. Epub 2021 Jul 8.
Drug Alcohol Depend. 2018 Sep 1;190:1-5. doi: 10.1016/j.drugalcdep.2018.05.018. Epub 2018 Jun 23.
4
Who tweets? Deriving the demographic characteristics of age, occupation and social class from twitter user meta-data.谁会发推文?从推特用户元数据中推导年龄、职业和社会阶层的人口统计学特征。
PLoS One. 2015 Mar 2;10(3):e0115545. doi: 10.1371/journal.pone.0115545. eCollection 2015.
5
Characterizing the followers and tweets of a marijuana-focused Twitter handle.对一个专注于大麻的推特账号的关注者和推文进行特征分析。
J Med Internet Res. 2014 Jun 27;16(6):e157. doi: 10.2196/jmir.3247.