Media ecology and artificial intelligence: A critical perspective on authorship and lessons learned from social media

As the field of media ecology enters a writing landscape influenced by AI, the awareness of the pitfalls and opportunities will be critical for responding to this phenomenon. Throughout this piece, the changing nature of authorship is explored. Social media platforms are used as a case study for understanding how AI technologies are changing the nature of content creation or media ecological artefacts. The piece concludes with a call to action for humans to remain part of the process to maintain and direct AI technologies towards more equitable and ethical outcomes.

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Publication Details

Journal
Explorations in Media Ecology
Published
2025-03-01
DOI
https://doi.org/10.1386/eme_00242_7
Citations
1
Primary Topic
Authorship Attribution and Profiling
Type
article
Field-Weighted Citation Impact
2.48
Controls
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article

Media ecology and artificial intelligence: A critical perspective on authorship and lessons learned from social media

Julianna Kirschner
1 citations
Explorations in Media Ecology
Authorship Attribution and Profiling
2.48
article

Media ecology and artificial intelligence: A critical perspective on authorship and lessons learned from social media

Julianna Kirschner
article en
1 citations

Abstract

As the field of media ecology enters a writing landscape influenced by AI, the awareness of the pitfalls and opportunities will be critical for responding to this phenomenon. Throughout this piece, the changing nature of authorship is explored. Social media platforms are used as a case study for understanding how AI technologies are changing the nature of content creation or media ecological artefacts. The piece concludes with a call to action for humans to remain part of the process to maintain and direct AI technologies towards more equitable and ethical outcomes.

Explorations in Media EcologyVol. 24(1)
University of Southern California (US)
Openalex Percentile: Top 9%
Authorship Attribution and Profiling
2.48
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