Data analytics as curatorial practice

Historians and specialists in both scientific areas of research and those of the humanities, have always been held in high regard for their knowledge of the past and their ability to understand and analyse the present by drawing on history. However, we have reached a point when no one individual can compete with the large databases and online platforms that hold thousands of years’ worth of information. Technology has advanced to unimaginable lengths, even our everyday gadgets are performing tasks that would be impossible for any single human to do at such speeds and with such high levels of efficiency. In previous posts, I have already discussed how technological advancements have permeated the world of the museums and galleries, and how the new digital era has brought about significant changes and dilemmas in the way photography is curated.

Description of the process of content curation as suggested by the Curata software developers. (Source: marcopini)

As a consequence of this fast-paced evolution, the definition of what constitutes a curator has also become ambiguous, the line between a professional and an amateur has become blurred. Today, social media is both a medium to convey a message and a message in itself, on several layers. Instagram, for example, is a visual, narrative and networked media platform for digital vernacular photography. It conveys an amalgam of messages and represents a fusion of what users are trying to transmit and what their content actually signifies. Here, each user is the curator of their own narrative, responsible for communicating an idea in a self-governed online gallery.

The value of Instagram lies in the fact that it offers an opportunity to study images not only as individual specimens of an art form, but also as parts of an interconnected whole. This network of connections is visible from the fact that all of these digital artefacts store information in the form of technical metadata (geo-spatial coordinates), and also in the form of tags that the users themselves attach to the pictures, a part of a wide-spread folksonomy. By collecting a large sample of photographs from this platform, it is possible to outline patterns which render a kind of extended reality. In contrary to Google Maps, which presents locations as they are, in an objective manner, Instagram and other social media platforms hold an endless amount of information that give a much more versatile, subjective overview of both everyday life events, but also ones of national or global impact.

“A radial plot visualization showing 23,581 photos uploaded to Instagram in Brooklyn area during Hurricane Sandy (29–30 November  2012). Photo’s distance from the center (radius) corresponds to its mean hue; photo’s angle (i.e. the position along the perimeter) corresponds to its time stamp. Note the demarcation line that reveals the moment of a power outage in the area and indicates the intensity of the shared experience (dramatic decrease in the number of photos, and their darker colors to the right of the line).” (Hochman and Manovich)  (Source: Phototrails)

For the past decade, the analysis of content in photographs has been extensively studied by both “the multimedia and vision research community. Today, several efficient region-based image retrieval engines are in use. Statistical modelling approaches have been proposed for automatic image annotation. Culturally significant pictures are being archived in digital libraries. Online photo sharing communities are becoming more and more common. In this age of digital picture explosion, it is critical to continuously develop intelligent systems for automatic image content analysis.” (Datta et al. 2) Building software that can distinguish between images of high and low aesthetic value and using computational approaches to outline what aspects of a photograph are most appealing to viewers, are more and more widespread practices.

Data mining techniques gather content from social media platforms and study large datasets containing information about cultural activities, opinions and conversations. By combining these with technical specifications of images (EXIF data) and features extracted from the photographic content and available metadata, it is possible not only to define what constitutes as a high-quality image, but also to understand cultural processes and their dynamics. Through graphing and analysing patterns, we are able to gain insight into what constitutes an aesthetically pleasing image not just in general, but to each individual person alike. The result of these analyses can be used to create data visualizations, which enable us to find possible meanings and interpretations of photographs and the way they are perceived.

“Eric Fischer (2010), “Locals and Tourists #2 (GTWA #1): New York.” The visualization compares locations of photos uploaded to Flickr and Picasa. Blue pictures are by locals. Red pictures are by tourists. Yellow pictures might be by either.” (Hochman and Manovich) (Source: #Eric Fischer)

We might still be some decades away from creating truly strong AI, but the mere range of tasks computers are able to perform grows exponentially and at a fascinating rate. By studying massive data sets humanity is now challenging its own theoretical concepts and assumptions of aesthetics. One day in the not too distant future, a machine might be more suitable for curatorial practice, by being able to analyse and distinguish what would be best fitting for an audience, based on past exhibitions and possible outcomes. In a photography gallery of the near future, we might even walk around with AR lenses that display a personalized exhibition based on our preferences gathered from the online sphere, governed by a machine that is capable of distinguishing what each individual might find aesthetically pleasing, informative or educationally sound.


Aydin, Tunc Ozan, Aljoscha Smolic, and Markus Gross. “Automated Aesthetic Analysis of Photographic Images.” IEEE Transactions on Visualization and Computer Graphics 21.1 (2015): 31–42. IEEE Xplore. Web.

Datta, Ritendra et al. “Studying Aesthetics in Photographic Images Using a Computational Approach.” Proceedings of the 9th European Conference on Computer Vision – Volume Part III. Berlin, Heidelberg: Springer-Verlag, 2006. 288–301. ACM Digital Library. Web. 9 Dec. 2016. ECCV’06.

Gibbs, Martin et al. “#Funeral and Instagram: Death, Social Media, and Platform Vernacular.” Information, Communication & Society 18.3 (2015): 255–268. Taylor and Francis+NEJM. Web.

Hochman, Nadav, and Lev Manovich. “Zooming into an Instagram City: Reading the Local through Social Media.” First Monday 18.7 (2013): n. pag. Web. 13 Dec. 2016.

Khanna, Parag and Ayesha. “Reaching the Singularity: It’s More Complicated Than We Think.” Big Think. N.p., 14 Oct. 2011. Web. 10 Dec. 2016.


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