Google Images, Climate Change, and the Disappearance of Humans

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Warren Pearce
Carlo De Gaetano


In this contribution, we present a visual approach to study the development of the online representation of climate change. We collected ranked image lists over a twelve years timespan on Google Image Search, and analyzed them with a two-fold visualization: an image timeline of the top 5 images per year and an area bump chart showing the top 10 tags automatically detected by the computer vision algorithm in the larger dataset of the top 100 results per year. We can draw two main conclusions from these results. First, the artificial separation between climate change and humans identified in previous studies of climate change imagery is being perpetuated and reinforced on one of the most important digital locations for visual culture: Google Images. Second, that there is a notable homogeneity within the corpus of images, as well as stability over time.

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Pearce, W., & De Gaetano, C. (2021). Google Images, Climate Change, and the Disappearance of Humans. Diseña, (19), Article.3.
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Author Biographies

Warren Pearce, University of Sheffield, Department of Sociological Studies

BA in Geography and Politics, University of Sheffield. MA in Public Policy and MA in Research Methods, University of Nottingham. Ph.D. in Public Policy, University of Nottingham. He is a Senior Lecturer in the Department of Sociological Studies at the University of Sheffield. Three areas are explored through his research: how science is used in public debates about politics and policy, with a focus on the use of scientific evidence, advice, and assessment in policy work; how digital platforms are changing experts and expertise; and the role of images in online science communication. Some of his most recent publications include ‘ Visual Cross-pla­tform Analysis: Digital Methods to Research Social Media Imagesʼ (with S. M. Özkula, A. K. Greene, L. Teeling, J. S. Bansard, J. J. Omena, and E. T. Rabello; Information, Communication & Society, Vol. 23, Issue 2) and ‘Learning the Lessons of Climatega­te: A Cosmopolitan Moment in the Public Life of Climate Science (with S. Raman; Wiley Interdisci­plinary Reviews: Climate Change, Vol. 11, Issue 6).

Carlo De Gaetano, Amsterdam University of Applied Sciences, Visual Methodologies Collective

MA in Communication Design, Politecnico di Milano. Information Designer and Digital Researcher at the Visual Methodologies Co­llective, Amsterdam University of Applied Sciences. He is a founding member of the Visual Methodologies Collective, working on data visualization for social research with an interest in the mapping of social issues through images and participatory practices. He also has a long-standing collaboration with the Digital Methods Initiative at the University of Amsterdam. He lectures on issue mapping at the Amsterdam Fashion Institute, in the MA Fashion Enterprise Creation. Some of his latest publications include ‘Dutch Political Instagramʼ (with G. Colombo; in The Politics of Social Media Manipulation, Amsterdam University Press, 2020), ‘Confronting Bias in the Online Represen­tation of Pregnancyʼ (with L. Bogers, S. Niederer, and F. Bardelli; Convergence, Vol. 26, Issue 5-6).


Brin, S., & Page, L. (1998). The Anatom of a Large-scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1), 107–117.

Corner, A., Webster, R., & Teriete, C. (2015). Climate Visuals: Seven Principles for Visual Climate Change Communication (Based on International Social Research). Climate Outreach.

Doyle, J. (2016). Celebrity Vegans and the Lifestyling of Ethical Consumption. Environmental Communication, 10(6), 777–790.

Fei-Fei, L., Fergus, R., & Perona, P. (2004). Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories. 2004 Conference on Computer Vision and Pattern Recognition Workshop, 178–178. IEEE.

Huang, D., Shan, C., Ardabilian, M., Wang, Y., & Chen, L. (2011). Local Binary Patterns and Its Application to Facial Image Analysis: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(6), 765–781.

Mauri, M., Elli, T., Caviglia, G., Uboldi, G., & Azzi, M. (2017). RAWGraphs: A Visualisation Platform to Create Open Outputs. Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter, 28:1-28:5.

Page, L., & Brin, S. (2004). Letter from the Founders “An Owner’s Manual” for Google’s Shareholders. USA: Securities and Exchange Commission.