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新冠疫情与非洲创新:运用推特数据和文本挖掘方法从坏事中发现好事

COVID-19 pandemic and African innovation: Finding the good from the bad using Twitter data and text mining approach.

作者信息

Asare Andy Ohemeng, Sarpong Eric Ohemeng, Truong Holds Ngoc, Osei-Bonsu Patrick, Ahado Samuel, Mensah William Gyasi

机构信息

Professor George Brown College 200 King Street East Toronto Ontario Canada.

University of Electronics Science and Technology of China No.2006, Xiyuan Avenue. West Hi-Tech. Zone Chengdu P.R. China.

出版信息

Int Soc Sci J. 2022 Nov 30. doi: 10.1111/issj.12386.

DOI:10.1111/issj.12386
PMID:36718201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9877784/
Abstract

This study investigates public sentiments and the essential topics of discussion on Africa's innovation amidst COVID-19. Web scraping techniques were used to collect and parse data from Twitter platform using the keywords "Africa Innovation COVID-19". A total of 54,318 cleaned English tweets were gathered and analysed using Twint Python Libraries. Our sentiment analysis findings revealed that 28,084 tweets (52 per cent) were positive, 21,037 (39 per cent), and 5197 (9 per cent) of tweets were neutral and negative, respectively, for Polarity sentiments. Notably, Healthcare, Imagination, Support, Webinar, Learning, Future, Rwanda, and Challenge were the most discussed topics on Africa's innovation during COVID-19. The topic labelling sentiments on the themes identified were positive, neutral, and negative, respectively. The study also revealed a cluster relationship between all identified topics. The relationship among these themes divulged how COVID-19 is positively shaping social and technological innovation in Africa. The study further presented practical implications to better position African leaders and policymakers to capitalise on the current innovation ecosystems and institutional capacities to transform the continent into a digital and innovation hub. The research concludes with theoretical recommendations and study limitations that will guide researchers and academicians in conducting future research in the subject area.

摘要

本研究调查了在新冠疫情期间公众对非洲创新的看法以及关键讨论话题。利用网络爬虫技术,通过关键词“非洲创新 新冠疫情”从推特平台收集并解析数据。使用Twint Python库收集并分析了总共54318条经过清理的英文推文。我们的情感分析结果显示,就极性情感而言,28084条推文(52%)为积极,21037条(39%)为中性,5197条(9%)为消极。值得注意的是,医疗保健、想象力、支持、网络研讨会、学习、未来、卢旺达和挑战是新冠疫情期间关于非洲创新讨论最多的话题。针对所确定主题的主题标签情感分别为积极、中性和消极。该研究还揭示了所有确定主题之间的聚类关系。这些主题之间的关系揭示了新冠疫情如何积极塑造非洲的社会和技术创新。该研究进一步提出了实际意义,以便更好地帮助非洲领导人和政策制定者利用当前的创新生态系统和机构能力,将非洲大陆转变为数字和创新中心。研究最后提出了理论建议和研究局限性,将指导研究人员和学者在该主题领域开展未来研究。

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本文引用的文献

1
An "Infodemic": Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak.一场“信息疫情”:利用大量推特数据来了解公众对2019年冠状病毒病疫情的早期情绪
Open Forum Infect Dis. 2020 Jun 30;7(7):ofaa258. doi: 10.1093/ofid/ofaa258. eCollection 2020 Jul.
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Coronavirus Disease 2019: In-Home Isolation Room Construction.2019冠状病毒病:居家隔离病房建设
A A Pract. 2020 Apr;14(6):e01218. doi: 10.1213/XAA.0000000000001218.
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Digital technologies in the public-health response to COVID-19.数字技术在应对 COVID-19 中的公共卫生响应。
Nat Med. 2020 Aug;26(8):1183-1192. doi: 10.1038/s41591-020-1011-4. Epub 2020 Aug 7.
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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.
5
Social media for rapid knowledge dissemination: early experience from the COVID-19 pandemic.用于快速知识传播的社交媒体:COVID-19大流行的早期经验
Anaesthesia. 2020 Dec;75(12):1579-1582. doi: 10.1111/anae.15057. Epub 2020 Mar 31.