Abstract
As one of the early COVID-19 epidemic outbreak areas, China attracted the global news media’s attention at the beginning of 2020. During the epidemic period, Chinese people united and actively fought against the epidemic. However, in the eyes of the international public, the situation reported about China is not optimistic. To better understand how the international public portrays China, especially during the epidemic, we present a case study with big data technology. We aim to answer three questions: (1) What has the international media focused on during the COVID-19 epidemic period? (2) What is the media’s tone when they report China? (3) What is the media’s attitude when talking about China? In detail, we crawled more than 280 000 pieces of news from 57 mainstream media agencies in 22 countries and made some interesting observations. For example, international media paid more attention to Chinese livelihood during the COVID-19 epidemic period. In March and April, “progress of Chinese vaccines,” “specific drugs and treatments,” and “virus outbreak in U.S.” became the media’s most common topics. In terms of news attitude, Cuba, Malaysia, and Venezuela had a positive attitude toward China, while France, Canada, and the United Kingdom had a negative attitude. Our study can help understand China’s image in the eyes of the international media and provide a sound basis for image analysis.
摘要
中国作为新冠肺炎疫情早期爆发地区之一, 在2020年初就引起全球新闻媒体关注. 疫情期间, 中国人民团结一致, 积极抗击疫情. 然而, 在国际公众眼中, 有关中国疫情的报道并不乐观. 为更好了解国际公众如何看待中国, 特别是在疫情期间, 我们利用大数据技术进行了案例研究. 我们主要想回答3个问题: (1) 新冠肺炎疫情期间, 国际媒体关注的焦点是什么? (2) 媒体报道中国时的立场是什么? (3) 媒体谈论中国时的态度是什么? 具体来说, 我们从22个国家的57家主流媒体中收集了28万则以上相关新闻, 从中分析出一些有趣现象. 例如, 新冠肺炎疫情期间, 国际媒体更加关注中国民生; 在3月和4月, “中国疫苗进展” “特定药物和治疗” “美国病毒爆发” 成为媒体最常见话题; 在新闻态度方面, 古巴、 马来西亚、 委内瑞拉对中国持正面态度, 而法国、 加拿大、 英国则持负面态度. 我们的研究有助于理解中国在国际媒体眼中的形象, 并为形象分析提供良好依据.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Data availability
The data that support the findings of this study are openly available at http://203.195.140.107/dataset/download.
References
Chen HM, Zhu ZY, Qi FC, et al., 2021. Country image in COVID-19 pandemic: a case study of China. IEEE Trans Big Data, 7(1):81–92. https://doi.org/10.1109/TBDATA.2020.3023459
D’Alessio D, Allen M, 2000. Media bias in presidential elections: a meta-analysis. J Commun, 50(4):133–156. https://doi.org/10.1111/j.1460-2466.2000.tb02866.x
Devlin J, Chang MW, Lee K, et al., 2019. BERT: pre-training of deep bidirectional transformers for language understanding. Proc Conf of the North American Chapter of the Association for Computational Linguistics, p.4171–4186.
Filloux F, 2013. Google News: the Secret Sauce. Monday Note. https://mondaynote.com/google-news-the-secret-sauce-3f1cec521209 [Accessed on Feb. 23, 2021].
Ghosh S, Singhania P, Singh S, et al., 2019. Stance detection in web and social media: a comparative study. Int Conf of the CLEF Association, p.75–87. https://doi.org/10.1007/978-3-030-28577-7_4
Honnibal M, Montani I, 2017. spaCy 2: Natural Language Understanding with Bloom Embeddings, Convolutional Neural Networks and Incremental Parsing. GitHub. https://github.com/xtrancea/spaCy [Accessed on Feb. 23, 2021].
Hou L, Li J, Wang Z, et al., 2015. NewsMiner: multifaceted news analysis for event search. Knowl-Based Syst, 76:17–29. https://doi.org/10.1016/j.knosys.2014.11.017
Hutto C, Gilbert E, 2014. Vader: a parsimonious rule-based model for sentiment analysis of social media text. Proc Int AAAI Conf on Web and Social Media, p.216–225.
John S, James L, 2018. Perceived Accuracy and Bias in the News Media. Knight Foundation. https://knightfoundation.org/wp-content/uploads/2020/03/KnightFoundation_AccuracyandBias_Report_FINAL.pdf [Accessed on Feb. 23, 2021].
Jurafsky D, Martin JH, 2000. Speech and Language Processing: an Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Prentice Hall, New Jersey, USA.
Kim Y, 2014. Convolutional neural networks for sentence classification. Proc Conf on Empirical Methods in Natural Language Processing, p.1746–1751.
Liu X, Li Q, Nourbakhsh A, et al., 2016. Reuters tracer: a large scale system of detecting & verifying real-time news events from Twitter. ACM Int Conf on Information and Knowledge Management, p.207–216. https://doi.org/10.1145/2983323.2983363
Lloyd L, Kechagias D, Skiena S, 2005. Lydia: a system for large-scale news analysis. Int Conf on String Processing and Information Retrieval, p.161–166. https://doi.org/10.1007/11575832_18
Lowe W, 2002. Software for Content Analysis—a Review. Harvard University. https://dl.conjugateprior.org/preprints/content-review.pdf [Accessed on Apr. 23, 2021].
Macnamara JR, 2005. Media content analysis: its uses, benefits and best practice methodology. Asia Pacif Publ Rel J, 6(1):1–34.
Manheim JB, Albritton RB, 1984. Changing national images: international public relations and media agenda setting. Am Pol Sci Rev, 78(3):641–657. https://doi.org/10.2307/1961834
McCarthy J, Titarenko L, McPhail C, et al., 2008. Assessing stability in the patterns of selection bias in newspaper coverage of protest during the transition from communism in Belarus. Mob Int Q, 13(2):127–146. https://doi.org/10.17813/maiq.13.2.u45461350302663v
Neri F, Aliprandi C, Capeci F, et al., 2012. Sentiment analysis on social media. IEEE/ACM Int Conf on Advances in Social Networks Analysis and Mining, p.919–926. https://doi.org/10.1109/ASONAM.2012.164
Nimmo DD, Savage RL, 1976. Candidates and Their Images: Concepts, Methods, and Findings. Goodyear Publishing Company, Pacific Palisades, USA.
Oelke D, Geißelmann B, Keim DA, 2012. Visual analysis of explicit opinion and news bias in German soccer articles. Int Workshop on Visual Analytics, p.49–53. https://doi.org/10.2312/PE/EuroVAST/EuroVA12/049-053
Peng Z, 2004. Representation of china: an across time analysis of coverage in the New York Times and Los Angeles Times. Asian J Commun, 14(1):53–67. https://doi.org/10.1080/0129298042000195170
Sayyadi H, Raschid L, 2013. A graph analytical approach for topic detection. ACM Trans Intern Technol, 13(2):4. https://doi.org/10.1145/2542214.2542215
Sun Y, Qiu H, Zheng Y, et al., 2020. SIFRank: a new baseline for unsupervised keyphrase extraction based on pre-trained language model. IEEE Access, 8:10896–10906. https://doi.org/10.1109/ACCESS.2020.2965087
Vaismoradi M, Turunen H, Bondas T, 2013. Content analysis and thematic analysis: implications for conducting a qualitative descriptive study. Nurs Health Sci, 15(3):398–405. https://doi.org/10.1111/nhs.12048
Wang H, 2003. National image building and Chinese foreign policy. China Int J, 1(1):46–72. https://doi.org/10.1142/S0219747203000050
Zhang L, 2010. The rise of China: media perception and implications for international politics. J Contemp China, 19(64):233–254. https://doi.org/10.1080/10670560903444199
Zhang L, Wu D, 2017. Media representations of China: a comparison of China Daily and Financial Times in reporting on the belt and road initiative. Crit Arts, 31(6):29–43. https://doi.org/10.1080/02560046.2017.1408132
Zhang Q, Yilmaz E, Liang S, 2018. Ranking-based method for news stance detection. The Web Conf, p.41–42. https://doi.org/10.1145/3184558.3186919
Acknowledgements
Special thanks to Yi FENG, Mingqi LAI, Rui ZHANG, Mohan ZHANG, Yu LI, and Haoshuang CAO for their efforts devoted to this project.
Author information
Authors and Affiliations
Contributions
Hong HUANG and Xuanhua SHI designed the research. Zhexue CHEN, Chenxu WANG, and Zepeng HE processed the data. Hong HUANG, Zhexue CHEN, and Chenxu WANG drafted the manuscript. Hai JIN, Mingxin ZHANG, and Zongya LI helped revise the manuscript. Hong HUANG finalized the paper.
Corresponding author
Ethics declarations
Hong HUANG, Zhexue CHEN, Xuanhua SHI, Chenxu WANG, Zepeng HE, Hai JIN, Mingxin ZHANG, and Zongya LI declare that they have no conflict of interest. This study is GDPR (General Data Protection Regulation) compliant.
Additional information
Hong HUANG, first author of this invited paper, is an associate professor at Huazhong University of Science and Technology (HUST), Wuhan China. She received her PhD degree in computer science from the University of Göttingen, Germany in 2016, and her ME degree in electronic engineering from Tsinghua University, Beijing, China in 2012. Her research interests include social network analysis, social influence, and data mining.
Xuanhua SHI, corresponding author of this invited paper, is currently a professor with the School of Computer Science and Technology, HUST, Wuhan, China. He is the deputy director of the National Engineering Research Center for Big Data Technology and System (NER-CBDTS). He published more than 100 peer-reviewed papers in conferences and journals such as ASPLOS, VLDB, ACM Trans Comput Syst, and IEEE Trans Parall Distr Syst. He is a corresponding expert of Front Inform Technol Electron Eng. He received research supports from several governmental and industrial organizations, such as the National Natural Science Foundation of China, Ministry of Science and Technology, Ministry of Education, and the European Union. His current research interests include cloud computing, big data processing, and AI systems.
Hai JIN received his PhD degree in computer engineering from HUST, Wuhan, China, in 1994. He worked at the University of Hong Kong from 1998 to 2000, and was a visiting scholar at the University of Southern California, Los Angeles, CA, USA from 1999 to 2000. He is currently the Cheung Kung Scholars Chair Professor of Computer Science and Engineering with HUST. He has coauthored 15 books, and published over 700 research articles. He is now serving as an editor of Front Inform Technol Electron Eng. He was awarded the German Academic Exchange Service Fellowship to visit the Technical University of Chemnitz, Germany in 1996, and was supported by the National Natural Science Foundation of China for Distinguished Young Scholars in 2001. He is a Fellow of China Computer Federation (CCF) and a Life Member of Association for Computing Machinery (ACM). His research interests include computer architecture, virtualization technology, cluster computing and cloud computing, peer-to-peer computing, network storage, and network security.
Rights and permissions
About this article
Cite this article
Huang, H., Chen, Z., Shi, X. et al. China in the eyes of news media: a case study under COVID-19 epidemic. Front Inform Technol Electron Eng 22, 1443–1457 (2021). https://doi.org/10.1631/FITEE.2000689
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2000689