Media Reports on COVID-19 Vaccinations: A Study of Topic Modeling in South Korea

Discrepancies in the portrayal of the COVID-19 vaccine in Chinese and US international media outlets: A corpus-based discursive news values analysis

This article compares the discursive construction of news values in English news reporting of China Daily and CNN about the COVID-19 vaccine, to reveal how the two media packaged the vaccine and improved the acceptance of the international community towards the vaccines made in the respective countries. Adopting corpus linguistic methods and Discursive news values analysis (DNVA) framework, this study examines news values through keywords, nomination strategies, and photographs. The results show that the two media outlets constructed the news value of Eliteness dominantly through a systematic combination of words and images, albeit in different ways. China Daily prominently consists of references to China's international communications in the production and application of the COVID-19 vaccine, at the same time packaging the COVID-19 vaccine as foreign aid to enhance diplomatic relations as well as protect and promote the order of the international community. In contrast, CNN chiefly demonstrates references to domestic medical experts and the well-known manufacturers of the COVID-19 vaccine, packaging the COVID-19 vaccine as the US' medical/commercial product. The respective ways in which they portray the COVID-19 vaccine have helped to bolster the acceptance of each country's vaccine by the international community.

Text classification technique for discovering country-based publications from international COVID-19 publications

Objective

The significant increase in the number of COVID-19 publications, on the one hand, and the strategic importance of this subject area for research and treatment systems in the health field, on the other hand, reveals the need for text-mining research more than ever. The main objective of the present paper is to discover country-based publications from international COVID-19 publications with text classification techniques.

Methods

The present paper is applied research that has been performed using text-mining techniques such as clustering and text classification. The statistical population is all COVID-19 publications from PubMed Central® (PMC), extracted from November 2019 to June 2021. Latent Dirichlet allocation (LDA) was used for clustering, and support vector machine (SVM), scikit-learn library, and Python programming language were used for text classification. Text classification was applied to discover the consistency of Iranian and international topics.

Results

The findings showed that seven topics were extracted using the LDA algorithm for international and Iranian publications on COVID-19. Moreover, the COVID-19 publications show the largest share in the subject area of "Social and Technology in COVID-19" at the international (April 2021) and national (February 2021) levels with 50.61% and 39.44%, respectively. The highest rate of publications at international and national levels was in April 2021 and February 2021, respectively.

Conclusion

One of the most important results of this study was discovering a common trend and consistency of Iranian and international publications on COVID-19. Accordingly, in the topic category "Covid-19 Proteins: Vaccine and Antibody Response," Iranian publications have a common publishing and research trend with international ones.