Assembling stories tweet by tweet: strategic narratives from Chinese authorities on Twitter during the COVID-19 pandemic

ABSTRACT

Combining computational and qualitative methods, this research presents a novel approach to the analysis of China’s digital diplomacy. The study explores the main strategic narratives disseminated by the Chinese Communist Party on Twitter during the first year of the COVID-19 pandemic. To identify the narratives, a sequential design was conducted. First, topic modelling was implemented to a sample of 189,708 tweets in English published by 163 Chinese authorities from January 1st, 2020, to March 11th, 2021. Second, the strategic narratives framework was applied to distinguish thematic and structural patterns among the most representative tweets of the main topics revealed. The findings expose how China tried to rationalise challenging events in accordance with its pre-established system and identity narratives. The antagonism to the West, the promotion of a new style of global leadership, the rejection of criticism, and the legitimation of projects abroad characterised China’s digital endeavours to influence international audiences.

KEYWORDS:

• China
• strategic narratives
• COVID-19 pandemic
• topic modelling
• Twitter

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author ([email protected]) upon reasonable request

Notes

1. Available at: https://www.fmprc.gov.cn/mfa_eng/wjb_663304/zwjg_665342/.

2. Available at: https://securingdemocracy.gmfus.org/hamilton-monitored-accounts-on-twitter/#china.

3. A list with the selected accounts is available in Appendix A.

4. For example, when set to return 16 topics (the amount considered appropriate in LDA), Non-negative Matrix Factorisation and Bertopic produced vaguer results, as the word clusters appeared more mixed and therefore less interpretable (see Appendix E at https://github.com/pant-research/China_StrNarratives_virtual_repository).

5. With default parameters. Random state was set in 42, and the vectoriser employed was Countvectorizer.

6. The stopwords considered were the default stopwords from Spacy plus some other repetitive characters: ‘rt’, ‘co’, ‘amp’, ‘https’, ‘t’ and ‘pron’; and words corresponding to the accounts of the Canadian media outlets ‘globenadmail’, ‘cbcnews’, ‘ctvnews’,‘globalnews’, that were frequently mentioned by just one user, the Chinese consulate in Calgary (@ChinaCGCalgary), regardless the content of the tweet.

7. ‘covid19’, ‘covid 19’, ‘covid-19’, covid 2019, coronavirus were merged into covid19, whereas ‘US’, ‘U.S.’, ‘USA’, ‘U.S.A.’, and ‘United States’ were transformed into ‘USA’. ‘Hong Kong’, ‘HongKong’ and ‘HK’ were joined in ‘HongKong’.

8. The American Federal Communications Commission designated Huawei a ‘national security threat’ on June 30th (McGabe, 2020).

9. ‘Medium’ surely refers to media after lemmatisation.

10. Available at the virtual repository attached to this manuscript: https://github.com/pant-research/China_StrNarratives_virtual_repository.

Additional information

Funding

The work was supported by the Ministerio de Ciencia e Innovación, project MISMIS-BIAS [PGC2018-096212-B-C32].