Polarised social media discourse during COVID-19 pandemic: evidence from YouTube

ABSTRACT

The onset of the COVID-19 pandemic has attracted significant attention on social media platforms as these platforms provide users unparalleled access to ‘information’ from around the globe. In spite of demographic differences, people have been expressing and shaping their opinions using social media on topics ranging from the plight of migrant workers to vaccine development. However, the social media induced polarisation owing to selective online exposure to information during the COVID-19 pandemic has been a major cause of concern for countries across the world. In this paper, we analyse the temporal dynamics of polarisation in online discourse related to the COVID-19. We use random network theory-based simulation to investigate the evolution of opinion formation in comments posted on different COVID-19-related YouTube videos. Our findings reveal that as the pandemic unfolded, the extent of polarisation in the online discourse increased with time. We validate our experimental model using real-world complex networks and compare consensus formation on these networks with equivalent random networks. This study has several implications as polarisation around socio-cultural issues in crises such as pandemic can exacerbate the social divide. The framework proposed in this study can aid regulatory agencies to take required actions and mitigate social media-induced polarisation.

KEYWORDS:

• Polarisation
• COVID-19 pandemic
• social media
• online opinion formation
• consensus formation

Disclosure statement

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

Notes

1 https://www.businessinsider.in/slideshows/miscellaneous/a-comprehensive-timeline-of-the-new-coronavirus-pandemic-from-chinas-first-covid-19-case-to-the-present/slidelist/74721133.cms#slideid=74721140.

2 https://www.worldometers.info/coronavirus/

3 https://www.un.org/en/un-coronavirus-communications-team/un-tackling-%E2%80%98infodemic%E2%80%99-misinformation-and-cybercrime-covid-19.

4 https://www.youtube.com/watch?v=K-51rP9drwU.

5 https://hongkongfp.com/2020/01/29/wuhan-coronavirus-15000-hong-kong-medical-workers-join-new-union-threaten-strike-unless-govt-closes-border/; https://hongkongfp.com/2020/04/17/explainer-after-months-of-protests-is-hong-kong-selectively-enforcing-covid-19-laws/.

6 https://www.semrush.com/blog/most-visited-websites/.

7 We observed that replies on comments were not directly related to the video. The replies were more about the comment on which they were posted and tend to change the direction of discourse. Hence the replies were irrelevant to be considered as a discourse for videos.

8 https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview.

9 Excel uses MPQA (Multi-Perspective Question Answering) subjectivity lexicon.

10 All the encounters occurring within agents may not be random and/or binary. Encounters can also occur along social connections (non-random) as well as with multiple neighbours at once (non-binary). However, simulation based on either of these approaches (complete mixing vs. social mixing) leads to similar opinion formation results (line 4, Page 6; Deffuant et al. 2000).

11 The selection criteria were that after the cleaning process there should not be a loss of more than 50% in number of comments and a minimum of 1500 comments are retained on each video.

12 https://www.youtube.com/watch?v=TPpoJGYlW54&t=11s.

Additional information

Funding

This work was supported by Ministry of Human Resource Development [grant number SPARC/2018-2019/P495/SL]. The authors are also thankful to Brij Disa Centre for Data Science and Artificial Intelligence at IIM Ahmedabad for supporting this work.