The Attitudinal Divisions in Cyberspace on Re-distributive Social Welfare

• In Event: Science, Technology, and Public Opinion

Abstract

The post-COVID period has witnessed a deeper division in societies across various social groups on a range of social problems. The waves of strikes across all sectors in the UK demonstrate the rage and pressure of the population regarding economic situations, resource distribution, and redistribution. Despite widespread support at the picket line, debates and disagreements on related issues are frequently found online. Under the pressures of surging living costs, the long-tail Brexit influence, and the tough recovery from an unprecedented pandemic, people are experiencing difficult times managing the realization of their own interests and benefits, as well as sympathizing with each other. We are witnessing a much more divided public sphere on social and political issues in the UK and across the world (e.g., Jungkunz, 2021; Rivas-de-Roca & García-Gordillo, 2022).

This study is among the first to conceptualize attitudinal social cleavages in cyberspace and theorize their manifestations and transformations, focusing on issues related to redistributive justice, welfare, and social entitlements in the context of post-COVID UK. Going beyond the traditional notion of political polarization, which is more partisan-based, this concept is more complex and issue-related. It draws from the socio-demographic features of the population; however, such attitudinal differences may not always be equal to demographic social group segregations. It is also more fluid and evolves more easily than changes in the demographic scenario of the population.

Empirically, redistributive issues referring to socio-economic benefits are ideal for such inquiries due to the fact that these issues are more individual-related, thus more likely to incur wide-range digital engagements. Meanwhile, these issues are relatively less sensitive or partisan-based compared to issues such as abortion and transgender disputes; therefore, they are less likely to have social desirability bias and other confounding problems. This paper takes debates on public forums (Reddit and Twitter) as an example, combining state-of-the-art machine learning and digital tracing methods to investigate the presentation, evolution, and variations of online attitudinal divisions related to redistributive social issues. It contributes to the existing knowledge of this crucial social phenomenon, as well as potential efforts aimed at bridging social cleavages in public opinion.

Author

• Yan Wang, University of Manchester