JOURNAL ARTICLE

Authorship, ownership, and ethics in datafied discourse on Instagram: New perspectives for online linguistic landscapes.

  • Published In: Linguistic Landscape: An International Journal (LL), 2024, v. 10, n. 4. P. 425 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: McInerney, Erin 3 of 3

Abstract

Datafication, or the translation of our everyday actions into quantifiable metrics, underwrites a wide set of contemporary discursive practices. With a specific focus on the social media platform Instagram, this paper analyzes mediatized landscape signs as 'datafied discourse' enmeshed in an entangled apparatus of platforms, algorithms, and online networks. Using a corpus of 404 public Instagram posts gathered from the Café de Flore geotag, I examine how the vernacular practices of geotagging, mediatization, and remediatization reflexively construct this ostensibly 'user-generated' landscape. I then consider the implications of these and other discursive practices occurring at the 'online-offline nexus' through the dimensions of authorship and ownership. Finally, amid a confluence of new LL work, I propose to orient scholarship toward an 'infrastructural' perspective, in which datafication, online platforms, and algorithms are understood to exert considerable influence over our landscapes, thus emerging as relevant to scholars engaged in any genre of LL analysis. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Linguistic Landscape: An International Journal (LL). 2024/11, Vol. 10, Issue 4, p425
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2024
  • ISSN:2214-9953
  • DOI:10.1075/ll.24078.mci
  • Accession Number:181644867
  • Copyright Statement:Copyright of Linguistic Landscape: An International Journal (LL) is the property of John Benjamins Publishing Co. 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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