Web data and computational models can play important roles in analyzing cultural trends. The current study presents an analysis of 23,492 sneaker images and metadata collected from a global reselling shop, StockX.com. Based on data encompassing 22 years from 1999 to 2020, we propose a sneaker design index that helps track changes in the design characteristics of sneakers using a contrastive learning method. Our data suggest that sneaker designs have been employing brighter colors and lower hue and saturation values over time. We also observe how popular brands have continued to build their unique identities in shape-related design space. The embedding analysis also predicts which sneakers will likely see a high premium in the reselling market, suggesting viable algorithm-driven investment and design strategies. The current work is one of the first publicly available studies to analyze product design evolution over a long historical period and has implications for the novel use of Web data to understand cultural patterns that are otherwise difficult to assess.

Billions of photos shared online today are created by people with different socio-economic characteristics living in different locations. We introduce a number of methods for quantifying the differences between such "photo cultures" and apply them to a large collection of Instagram images shared in five mega-cities around the world. First, we extract image content and style features and use them to design a new visualization technique for qualitative analysis of photo cultures. We then use supervised learning to automatically recognize and compare visual activity at different locations and expose surprising connections between geographically distant photo cultures. Finally, we perform a low-level quantitative analysis to understand what makes photo cultures different from each other.

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.

How can we use computational analysis and visualization of content and interactions on social media network to write histories? Traditionally, historical timelines of social and political upheavals give us only distant views of the events, and singular interpretation of a person constructing the timeline. However, using social media as our source, we can potentially present many thousands of individual views of the events. We can also include representation of the everyday life next to the accounts of the exceptional events. This paper explores these ideas using a particular case study - images shared by people in Kiev on Instagram during 2014 Ukranian Revolution. Using Instagram public API we collected 13208 geo-coded images shared by 6165 Instagram users in the central part of Kiev during February 17-22, 2014. We used open source and our own custom software tools to analyze the images along with upload dates and times, geo locations, and tags, and visualize them in different ways.

Throughout the human history, the design of different cultural techniques and media forms for representing human knowledge, collective and personal experience, and what we now call data have not been confined to single individuals or disciplines. To mention just a few examples, natural languages, printed books, a linear perspective, landscape photography, and documentary cinema have all been developed and refined over time by whole societies and multiple individuals.Today with the computer acting as the interface for all past, present, and emergent forms of media, the situation is quite different. Computer scientists working on media computing play the key role in how our societies will remember their histories, how we will represent ourselves and others, what we will imagine and what metaphors we use to understand reality. In short, to work on media computing today is to assume big cultural responsibility - and also have tremendous power to define new forms of media.Unfortunately, more often than not, computer scientists do not take advantage of their powers. Too often, they simply translate existing media forms and cultural techniques into software interfaces. For instance, the controls of software media players are modeled after VCR; Acrobat software tries to recreate the conventions of a printed page; etc. This made sense twenty years ago when people were coming to computers after having first experienced other media technologies --- but not today.While I do not want to position the whole field of digital art as more innovative in this respect, one can point at a number of artists who have dedicated their careers to use computers to invent substantially new forms of media, often with very exiting results. In my talk I will show and discuss a number of works by these artists. I will also talk about the selected historical moments than a new media form emerged to see what we can learn from these histories.