Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2022

Abstract

Effective social data governance rests on a bedrock of social support. Without securing trust from the populace whose information is being collected, analyzed, and deployed, policies on which such data are based will be undermined by a lack of public confidence. The COVID-19 pandemic has accelerated digitalization and datafication by governments for the purposes of contact tracing and epidemiological investigation. However, concerns about surveillance and data privacy have stunted the adoption of such contact-tracing initiatives. This commentary analyzes Singapore's contact-tracing initiative to uncover the reasons for public resistance and efforts by the state to address them. The government's contact-tracing program encompassing its proprietary TraceTogether app and physical token initially triggered vociferous public criticisms of Big Brother style surveillance. Using a dialogic communication framework, we analyze the TraceTogether initiative to interrogate the communicative strategies that were used to overcome public resistance. We argue that these strategies reflect a top-down approach that prioritizes transactional dissemination of information, in line with Singapore's technocratic stance toward governance. We further assert that such communicative tactics represent missed opportunities to foster public confidence in social data governance through greater trust building. We propose solutions for more dialogic communicative forms that build trust, so that officials can develop a sound understanding of the public concerns, increase the level of public engagement, and incorporate public feedback into policies that govern data use.

Keywords

communicative strategies, contact tracing, COVID-19, Data governance, dialogic communication, TraceTogether

Discipline

Communication Technology and New Media | Databases and Information Systems | Health Communication | Public Health

Research Areas

Integrative Research Areas

Publication

Big Data and Society

Volume

9

Issue

1

First Page

1

Last Page

5

ISSN

2053-9517

Identifier

10.1177/20539517221104086

Publisher

SAGE

Embargo Period

2-23-2023

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

Additional URL

https://doi.org/10.1177/20539517221104086

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