COVID-19 Discourses in Traditional and Social Media in India and the United Kingdom

pixel6propix / unsplash

COVID-19 has had an enormous impact on the entire world since the WHO declared it a pandemic in March 2020. Yet, individual countries’ responses to the situation varied. Linguistic discourse analysis of English-language social media and newspapers can provide insights into India’s and Indians’ reactions to the pandemic. Moreover, it may provide knowledge about improving the response to comparable future situations.

Keywords will be collected from Twitter and selected newspapers that were posted or published during a target period (January 2020 to September 2022) in India. This will be subsequently compared with the existing data from the UK in previous studies. Unlike Germany and the UK, where data exists, India and the UK have contrastive economical structures. This approach will yield results on the question of how an economically developing nation responded to the pandemic. Discourse in English will be used in the analysis, which allows for a more direct linguistic contrast.

Twitter is a social media platform where users can interact in short messages and receive news about current events. Due to its interactive nature, the platform provides a meaningful reflection of users’ opinions [1]. Although it is not representative of the entire population, discourse on the platform has a considerable influence on shaping public opinion [1]. Furthermore, the network has been used for discourse analyses around the topic of COVID-19 in the past (e.g., [2]). Newspaper articles are of similar interest since including the highest-circulation newspapers in the present project means they also have a significant influence on public opinion.

Furthermore, analyzing public opinion around and responding to the pandemic may provide suggestions on how to cope with similar situations in the future. For instance, this can lead us to conclusions about reasons for vaccine hesitancy, or more generally, attitudes toward vaccines [2]. Since herd immunity is an essential factor in battling disease outbreaks, this is of great significance for society. Additionally, attitudes towards COVID-19 are a divisive topic in society and a factor of polarization [2]. 

Research Questions:

1. To what extent are the components of the keyword lists from the two countries distinctive or similar?
2. Does the type of platform, i.e., social media and newspaper, influence the choice of words in expressing one’s attitude towards the pandemic situation? 
3. How has the word frequency developed during the target period? 

Data will be extracted from LexisNexis and Twitter, where many news articles and Tweets are available respectively. Since the study will make use of existing keyword lists from the UK generated by previous research results (provided by the mentor of this project), only the Indian data will be collected. Target data will be narrowed down to five newspapers with the highest circulation rates in India. Following, words will be lemmatized in Python, segmented, and counted, creating a keyword list, and calculating each word’s frequency per million words. The comparison of the data from India and the UK will be initialized by comparing the data visually in R. We will then statistically explore the differences and similarities between the countries using log-likelihood and log-ratio. We aim to publish our results in a leading journal in the field of World Englishes (e.g., English World-Wide, English Language & Linguistics).

Unlike other studies of discourse around the COVID-19 pandemic, this project will not search Twitter and newspapers for a predetermined list of keywords [2]. Rather, it will employ a data-driven approach to generate keyword lists from the data [1]. Combining self-directed work with the comparison to existing data and results, we, as the students participating in the project, will gain valuable experience in data management and analysis, thus building our data literacy skills. All datasets that are collected during the investigation will be stored in OSF, an online data platform. This implies that our results will always be accessible and reusable.

Und hier die Studierendengruppe in ihren eigenen Worten:

Literatur:

• [1] Fuchs, R. (2022). Viral Discourses – How We Discuss COVID-19 [Conference talk].
• [2] Guntuku, S. C., Buttenheim, A. M., Sherman, G., & Merchant, R. M. (2021). Twitter discourse reveals geographical and temporal variation in concerns about covid-19 vaccines in the United States. Vaccine, 39(30), 4034–4038. https://doi.org/10.1016/j.vaccine.2021.06.014