Sorting a public? Using quali-quantitative methods to interrogate the role of algorithms in digital democracy platforms

Abstract

Following concerns about social media’s role in politics (fostering polarization and spreading disinformation), many activists and civic hackers have developed alternative digital democracy platforms for both deliberation and the representation of public opinion. But how are we to study the role of these platforms, and in particular, their algorithms in the development of issues and the publics that gather around them? This article employs a simple quali-quantitative data visualization to study how a particular digital democracy platform, vTaiwan (an implementation of Pol.is – a tool for generating opinions and consensus about public issues) – formats political participation. We investigate how one particular issue (Uber legalization) was formed and reformed by users, moderators, and algorithms on the vTaiwan platform over time. while the algorithm sorted opinions into a binary of pro and anti-Uber positions, we find that the comments themselves and their sequence suggest more nuanced positions and the potential for dialogue. We argue that vTaiwan may be limited by its focus on simple quantitative data points (positive or negative votes as opposed to the texts themselves) and a forced separation of participants into in-or-out opinion groups. This study contributes to critical algorithm studies and digital democracy studies by offering an effective way to analyse the role of algorithms in democratic politics.

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ORCID for Yu-Shan Tseng: orcid.org/0000-0003-3728-314X

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