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  Interpreting Climate Images on the Internet: Mixing Algorithmic and Interpretive Views to Enable an Intercultural Comparison (ANCI)

Schneider, B., Nocke, T., Heinicker, P., Kienbaum, J. (2023): Interpreting Climate Images on the Internet: Mixing Algorithmic and Interpretive Views to Enable an Intercultural Comparison (ANCI). - In: Schneider, B., Löffler, B., Mager, T., Hein, C. (Eds.), Mixing Methods: Practical Insights from the Humanities in the Digital Age, (Digital Humanities Research ; 7), Bielefeld : transcript, 189-211.
https://doi.org/10.14361/9783839469132-020

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Datensatz-Permalink: https://publications.pik-potsdam.de/pubman/item/item_29068 Versions-Permalink: https://publications.pik-potsdam.de/pubman/item/item_29068_1
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 Urheber:
Schneider, Birgit1, Autor
Nocke, Thomas2, Autor              
Heinicker, Paul1, Autor
Kienbaum, Janna1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Countless climate images are in circulation on the internet, such as burning globes, polar bears and photos of global climate impacts. These images are networked and generate a specific view of climate change. Our case study engages in an intercultural image comparison based on Google Image queries. As an interdisciplinary team of experts drawn from art his- tory, media studies, interface design and computer graphics, our goal was to use a combination of qualitative and quantitative image analyses to explore the predominant discourses of digi- tised visual climate communication on the web. To this end, we automated the analysis of dif- ferent formal features of climate images (such as colour values, density and composition) with the aid of computer-driven methods (such as computer vision and machine learning) to build a corpus of thousands of images. Our focus was on image similarities, a concept shared by both image theory and computer analysis. In this chapter, we elucidate the outcome of our research on a conceptual and technical basis. The core issue addressed here is the manner in which art- historical methods (such as iconography and the concept of visual framing) are transformed when using computer-generated methods of computer vision and machine learning to anal- yse image similarities. This chapter focuses on our various insights while also reflecting on the general question of networked images on a methodological level. Ultimately, we were able to identify the promising potential but also the key limits of algorithmic image recognition and sorting when using machine learning to study images on the internet.

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Sprache(n): eng - Englisch
 Datum: 2023-09-272023-09-27
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.14361/9783839469132-020
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Model / method: Machine Learning
Model / method: Quantitative Methods
Model / method: Qualitative Methods
Regional keyword: Global
OATYPE: Gold Open Access
 Art des Abschluß: -

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Quelle 1

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Titel: Mixing Methods: Practical Insights from the Humanities in the Digital Age
Genre der Quelle: Buch
 Urheber:
Schneider, Birgit1, Herausgeber
Löffler, Beate1, Herausgeber
Mager, Tino1, Herausgeber
Hein, Carola1, Herausgeber
Affiliations:
1 External Organizations, ou_persistent22            
Ort, Verlag, Ausgabe: Bielefeld : transcript
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 189 - 211 Identifikator: DOI: 10.14361/9783839469132
ISBN: 978-3-8394-6913-2

Quelle 2

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Titel: Digital Humanities Research
Genre der Quelle: Reihe
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 7 Artikelnummer: - Start- / Endseite: - Identifikator: -