Social network analysis of Twitter data from Pakistan during COVID-19

IF 2.1 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE
Syeda Hina Batool, W. Ahmed, K. Mahmood, Ashraf Sharif
{"title":"Social network analysis of Twitter data from Pakistan during COVID-19","authors":"Syeda Hina Batool, W. Ahmed, K. Mahmood, Ashraf Sharif","doi":"10.1108/idd-03-2021-0022","DOIUrl":null,"url":null,"abstract":"\nPurpose\nThe use of social media has increased during the COVID-19 pandemic. Social media platforms provide opportunities to share news, ideas and personal stories. Twitter is used by citizens in Pakistan to respond and comment on emerging news stories and events. However, it is not known whether Twitter played a positive or negative role in spreading updates and preventive messages during the COVID-19 pandemic. The purpose of this study is to analyse content from Twitter during the pandemic.\n\n\nDesign/methodology/approach\nNodeXL was used to retrieve data using the keyword وائرس کورونا (written in Urdu and which translates to Coronavirus). The first data set (Case Study 1) was based on 10,284 Twitter users from the end of March. The second data set (Case Study 2) was based on 10,644 Twitter users from the start of April. The theoretical lens of effective message framing was used to classify the most retweeted content on Twitter.\n\n\nFindings\nTwitter was used for personal and professional projections and included certain tweets included political motives even during the unfolding health crisis. There appeared to be very few successful attempts to use Twitter as a tool for health awareness and risk communication. The empirical findings indicate that the most retweeted messages were gain-framed and can be classified as personal, informative and political in nature.\n\n\nOriginality/value\nThe present study provides insights likely to be of interest to researchers, health organizations, citizens, government and politicians that are interested in making more effective use of social media for the purposes of health promotion. The authors also provide novel insights into the key topics of discussions, websites and hashtags used by Pakistani Twitter users during the COVID-19 pandemic.\n","PeriodicalId":43488,"journal":{"name":"Information Discovery and Delivery","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2021-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Information Discovery and Delivery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/idd-03-2021-0022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 1

Abstract

Purpose The use of social media has increased during the COVID-19 pandemic. Social media platforms provide opportunities to share news, ideas and personal stories. Twitter is used by citizens in Pakistan to respond and comment on emerging news stories and events. However, it is not known whether Twitter played a positive or negative role in spreading updates and preventive messages during the COVID-19 pandemic. The purpose of this study is to analyse content from Twitter during the pandemic. Design/methodology/approach NodeXL was used to retrieve data using the keyword وائرس کورونا (written in Urdu and which translates to Coronavirus). The first data set (Case Study 1) was based on 10,284 Twitter users from the end of March. The second data set (Case Study 2) was based on 10,644 Twitter users from the start of April. The theoretical lens of effective message framing was used to classify the most retweeted content on Twitter. Findings Twitter was used for personal and professional projections and included certain tweets included political motives even during the unfolding health crisis. There appeared to be very few successful attempts to use Twitter as a tool for health awareness and risk communication. The empirical findings indicate that the most retweeted messages were gain-framed and can be classified as personal, informative and political in nature. Originality/value The present study provides insights likely to be of interest to researchers, health organizations, citizens, government and politicians that are interested in making more effective use of social media for the purposes of health promotion. The authors also provide novel insights into the key topics of discussions, websites and hashtags used by Pakistani Twitter users during the COVID-19 pandemic.
COVID-19期间巴基斯坦Twitter数据的社交网络分析
目的在2019冠状病毒病大流行期间,社交媒体的使用有所增加。社交媒体平台提供了分享新闻、想法和个人故事的机会。巴基斯坦公民使用Twitter来回应和评论新出现的新闻故事和事件。但是,在新冠疫情期间,推特在传播最新信息和预防信息方面发挥了积极还是消极的作用,目前尚不清楚。本研究的目的是分析大流行期间Twitter的内容。设计/方法/方法使用nodexl检索数据,使用关键字وائرس کورونا(用乌尔都语编写,翻译为冠状病毒)。第一个数据集(案例研究1)基于3月底的10284个Twitter用户。第二个数据集(案例研究2)基于4月初的10644名Twitter用户。利用有效信息框架的理论视角对Twitter上转发次数最多的内容进行分类。twitter被用于个人和专业预测,甚至在健康危机期间,某些推文也包含政治动机。似乎很少有成功的尝试将推特作为提高健康意识和风险沟通的工具。实证研究结果表明,转发最多的信息是利益框架的,可以分为个人、信息和政治三类。原创性/价值本研究为研究人员、卫生组织、公民、政府和政治家提供了可能感兴趣的见解,他们对更有效地利用社交媒体促进健康感兴趣。作者还对2019冠状病毒病大流行期间巴基斯坦Twitter用户使用的讨论、网站和标签的关键话题提供了新颖的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Information Discovery and Delivery
Information Discovery and Delivery INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
5.40
自引率
4.80%
发文量
21
期刊介绍: Information Discovery and Delivery covers information discovery and access for digital information researchers. This includes educators, knowledge professionals in education and cultural organisations, knowledge managers in media, health care and government, as well as librarians. The journal publishes research and practice which explores the digital information supply chain ie transport, flows, tracking, exchange and sharing, including within and between libraries. It is also interested in digital information capture, packaging and storage by ‘collectors’ of all kinds. Information is widely defined, including but not limited to: Records, Documents, Learning objects, Visual and sound files, Data and metadata and , User-generated content.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信