JOURNAL ARTICLE
Online democracy: Applying Hannah Arendt's model of democracy to the internet.
Published In: Theoria: A Swedish Journal of Philosophy, 2023, v. 89, n. 6. P. 856 1 of 3
Database: Humanities Source Ultimate 2 of 3
Authored By: Bláhová, Sylvie 3 of 3
Abstract
The internet is a major part of our lives today. This applies to politics as well, and accordingly, the question of whether it is possible to realize democracy on the internet has arisen. Using the arguments of Hannah Arendt, the paper aims to determine what online democracy should look like. It is argued that the internet's decentralized structure is advantageous because it facilitates the implementation of the Arendtian system of political councils. Due to the character of online political platforms – mainly social media – these political councils should ideally revolve around shared issues that simultaneously create the common world on the internet. At the same time, clear rules need to be laid down for the functioning of these online political councils. Based on Arendt's arguments, it is claimed in the paper that these rules include the principles of mutual promises and covenants on the issues themselves. It is also argued that because Arendt emphasizes the role of appearing in the public sphere, the process of authentication – that is, verifying that there is a concrete person with a physical body behind each online account that wants to actively participate in a particular online political council – is required. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Theoria: A Swedish Journal of Philosophy. 2023/12, Vol. 89, Issue 6, p856
- Document Type:Article
- Subject Area:History
- Publication Date:2023
- ISSN:00405825
- DOI:10.1111/theo.12501
- Accession Number:174912777
- Copyright Statement:Copyright of Theoria: A Swedish Journal of Philosophy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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