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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.

新闻媒体眼中的中国：新冠肺炎疫情下的案例研究

[1]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.

[2]D’Alessio D, Allen M, 2000. Media bias in presidential elections: a meta-analysis. J Commun, 50(4):133-156.

[3]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.

[4]Filloux F, 2013. Google News: the Secret Sauce. Monday Note. https://mondaynote.com/google-news-the-secret-sauce-3f1cec521209 [Accessed on Feb. 23, 2021].

[5]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.

[6]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].

[7]Hou L, Li J, Wang Z, et al., 2015. NewsMiner: multifaceted news analysis for event search. Knowl-Based Syst, 76:17-29.

[8]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.

[9]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].

[10]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.

[11]Kim Y, 2014. Convolutional neural networks for sentence classification. Proc Conf on Empirical Methods in Natural Language Processing, p.1746-1751.

[12]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.

[13]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.

[14]Lowe W, 2002. Software for Content Analysis—a Review. Harvard University. https://dl.conjugateprior.org/preprints/content-review.pdf [Accessed on Apr. 23, 2021].

[15]Macnamara JR, 2005. Media content analysis: its uses, benefits and best practice methodology. Asia Pacif Publ Rel J, 6(1):1-34.

[16]Manheim JB, Albritton RB, 1984. Changing national images: international public relations and media agenda setting. Am Pol Sci Rev, 78(3):641-657.

[17]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.

[18]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.

[19]Nimmo DD, Savage RL, 1976. Candidates and Their Images: Concepts, Methods, and Findings. Goodyear Publishing Company, Pacific Palisades, USA.

[20]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.

[21]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.

[22]Sayyadi H, Raschid L, 2013. A graph analytical approach for topic detection. ACM Trans Intern Technol, 13(2):4.

[23]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.

[24]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.

[25]Wang H, 2003. National image building and Chinese foreign policy. China Int J, 1(1):46-72.

[26]Zhang L, 2010. The rise of China: media perception and implications for international politics. J Contemp China, 19(64):233-254.

[27]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.

[28]Zhang Q, Yilmaz E, Liang S, 2018. Ranking-based method for news stance detection. The Web Conf, p.41-42.