photos of DATA DRIFT exhibition curated by Lev Manovich, Rasa SMITE and Raitis SMITS


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DATA DRIFT

Exhibition website: http://rixc.org/en/festival/DATA%20DRIFT/

Dates: October 10 – November 22, 2015

Venue: kim? Contemporary Art Centre (Riga, Latvia)

Curators: Lev MANOVICH, Rasa SMITE and Raitis SMITS

The exhibition is organized by RIXC, The Center for New Media Culture, Riga (http://rixc.org/en/center/)


DATA DRIFT exhibition presents a number of works by some of the most influential data designers of our time, as well as by artists who use data as their artistic medium. How can we use the data medium to represent our complex societies, going beyond "most popular," and "most liked"? How can we organize the data drifts that structure our lives to reveal meaning and beauty? (And can we still think of "beauty" given our growing concerns with privacy and commercial uses of data we share?) How to use big data to "make strange," so we can see past and present as unfamiliar and new?

Artists: SPIN Unit (EU), Moritz STEFANER (DE), Frederic BRODBECK (DE), Kim ALBRECHT (DE), Boris MÜLLER (DE), Marian DÖRK (DE), Benjamin GROSSER (US), Maximilian SCHICH (DE/US), Mauro MARTINO (IT/US), Periscopic (US), Pitch Interactive (US), Smart Citizen Team (ES), Lev MANOVICH / Software Studies Initiative (US), Daniel GODDEMEYER (DE/US), Dominikus BAUR (DE), Mehrdad YAZDANI (US), Alise TIFENTALE (LV/US), Jay CHOW (US), Semiconductor (UK), Rasa SMITE, Raitis SMITS/RIXC (LV), Martins RATNIKS (LV), Kristaps EPNERS (LV).


Exhibition photos: Lev Manovich and Kristine Madjare (http://www.kmadjare.com)

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Our new paper "Predicting Social Trends from Non-photographic Images on Twitter" accepted for IEEE 2015 Big Data Conference



Random sample of Twitter images from 2013 labeled by GoogLeNet deep learning model as web sites and texts. We call refer to these images as "image-texts." The category includes screen shots of text chats, other types of texts and other kinds of non-photographic images. We found that the frequencies of these images are correlated with well-being responses from Gallup surveys, and also median housing prices, incomes, and education levels.


Johannes Vermeer. Woman in Blue Reading a Letter. 1663-1664.



Our new paper has been accepted for Big Data and the Humanities workshop at IEEE 2015 Big Data Conference:



Predicting Social Trends from Non-photographic Images on Twitter

Mehrdad Yazdani (California Institute for Telecommunication and Information) and Lev Manovich (The Graduate Center, City University of New York)



Abstract

Humanists use historical images as sources of information about social norms, behavior, fashion, and other details of particular cultures, places and periods. Dutch Golden Era paintings, works by French Impressionists, and 20th century street photography are just three examples of such images. Normally such visuals directly show objects of interests such as social scenes, city streets, or peoples dresses. But what if masses of images shared on social networks contain information about social trends even if these images do not directly represent objects of interest? This is the question we investigate in our study.

In the last few years researchers have shown that aggregated characteristics of large volumes of social media are correlated with many socio-economic characteristics and can also predict a range of social trends. The examples include flu trends, success of movies, and measures of social well-being of populations. Nearly all such studies focus on text content, such as posts on Twitter and Facebook.

In contrast, we focus on images. We investigate if features extracted from Tweeted images can predict a number of socio-economic characteristics. Our dataset is one million images shared on Twitter during one year in 20 different U.S. cities. We classify the content of these images using the state-of-the-art Convolutional Neural Network GoogLeNet and then select the largest category that we call “image-texts” - non-photographic images that are typically screen shots of websites or text-message conversations. We construct two features describing patterns in image-texts: aggregated sharing rate per year per city, and the sharing rate per hour over a 24-hour period aggregated over one year in each city.

We find that these features are correlated with self-reported social well-being responses from Gallup surveys, and also median housing prices, incomes, and education levels. These results suggest that particular types of social media images can be used to predict social characteristics not readily detectable in images.


The full paper will be available online after IEEE 2015 Big Data conference.

Data Drift exhibition co-curated by Lev Manovich opens in Riga (Latvia) 10/05/2015



screenshot from CULTUREGRAPHY (2014) by Kim Albrecht, one of the projects shown in Data Drift exhibition


DATA DRIFT exhibition

Exhibition website: http://rixc.org/en/festival/DATA%20DRIFT/

Dates: October 10 – November 22, 2015

Opening: October 9 at 19:00

Venue: kim? Contemporary Art Centre in Riga


DATA DRIFT exhibition presents a number of works by some of the most influential data designers of our time, as well as by artists who use data as their artistic medium. How can we use the data medium to represent our complex societies, going beyond "most popular," and "most liked"? How can we organize the data drifts that structure our lives to reveal meaning and beauty? (And can we still think of "beauty" given our growing concerns with privacy and commercial uses of data we share?) How to use big data to "make strange," so we can see past and present as unfamiliar and new?

If painting was the art of the classical era, and photograph that of the modern era, data visualization is the medium of our own time. Rather than looking at the outside worldwide and picturing it in interesting ways like modernist artists (Instagram filters already do this well), data designers and artists are capturing and reflecting on the new data realities of our societies.

Curated by Lev MANOVICH, Rasa SMITE and Raitis SMITS, the exhibition will feature artworks and data visualization by SPIN Unit (EU), Moritz STEFANER (DE), Frederic BRODBECK (DE), Kim ALBRECHT (DE), Boris MÜLLER (DE), Marian DÖRK (DE), Benjamin GROSSER (US), Maximilian SCHICH (DE/US), Mauro MARTINO (IT/US), Periscopic (US), Pitch Interactive (US), Smart Citizen Team (ES), Lev MANOVICH / Software Studies Initiative (US), Daniel GODDEMEYER (DE/US), Dominikus BAUR (DE), Mehrdad YAZDANI (US), Alise TIFENTALE (LV/US), Jay CHOW (US), Semiconductor (UK), Rasa SMITE, Raitis SMITS/RIXC (LV), Martins RATNIKS (LV), Kristaps EPNERS (LV).

The exhibition website includes information and images of all shown artworks.

The Exhibition Opening program includes public talk in the Renewable Futures conference by the exhibition curator Lev MANOVICH, that will take place on October 9th at 17:00 at the Stockholm School of Economics in Riga. The talk will be followed by the official opening of the exhibition at 19:00 in the kim? Contemporary Art Center.

The DATA DRIFT exhibition is featured event in this year's RIXC Art Science Festival program, taking place in Riga from October 8 to 10, 2015.

The DATA DRIFT exhibition will be open from October 10 to November 22, 2015 in Riga.

Venue: kim? Contemporary Art Centre gallery, Maskavas street 12/1, Spikeri Creative Quartier, Riga, Latvia.

Opening hours: Mon – closed, Tue 12:00–20:00 (free entrance), Wed–Sun 12:00-18:00.

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