Pinterest is an image-based online social network, which was launched in the year 2010 and has gained a lot of traction, ever since. Within 3 years, Pinterest has attained 48.7 million unique users. This stupendous growth makes it interesting to study Pinterest, and gives rise to multiple questions about it's users, and content. We characterized Pinterest on the basis of large scale crawls of 3.3 million user profiles, and 58.8 million pins. In particular, we explored various attributes of users, pins, boards, pin sources, and user locations, in detail and performed topical analysis of user generated textual content. The characterization revealed most prominent topics among users and pins, top image sources, and geographical distribution of users on Pinterest. We then investigated this social network from a privacy and security standpoint, and found traces of malware in the form of pin sources. Instances of Personally Identifiable Information (PII) leakage were also discovered in the form of phone numbers, BBM (Blackberry Messenger) pins, and email addresses. Further, our analysis demonstrated how Pinterest is a potential venue for copyright infringement, by showing that almost half of the images shared on Pinterest go uncredited. To the best of our knowledge, this is the first attempt to characterize Pinterest at such a large scale.
Although there has been a lot of work done on the network structure, and user characteristics on various social networks, to the best of our knowledge, little work has been done on image-based social networks; in particular, Pinterest. @cite_21 used Cultural Analytics visualization techniques to study 550,000 images taken by users of the social photo sharing application, Instagram. Authors of this work compared the images from New York City and Tokyo, and found differences in local color usage, cultural production rate, etc. harvested geo-referenced and tagged metadata associated with 8 million Flickr images, and considered how large numbers of people named city core areas @cite_32 . Authors of this work exploited the fact that the terms used to describe city centers, such as Downtown, are key concepts in everyday or vernacular language. The study covered six cities around the world, viz. Zurich, London, Sheffield, Chicago, Seattle, and Sydney.
@cite_21 @cite_32
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Picture-taking has never been easier. We now use our phones to snap photos and instantly share them with friends, family and strangers all around the world. Consequently, we seek ways to visualize, analyze and discover concealed socio-cultural characteristics and trends in this ever-growing flow of visual information. How do we then trace global and local patterns from the analysis of visual planetary–scale data? What types of insights can we draw from the study of these massive visual materials? In this study we use Cultural Analytics visualization techniques for the study of approximately 550,000 images taken by users of the location-based social photo sharing application Instagram. By analyzing images from New York City and Tokyo, we offer a comparative visualization research that indicates differences in local color usage, cultural production rate, and varied hue’s intensities— all form a unique, local, ‘Visual Rhythm’: a framework for the analysis of location-based visual information flows. 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. Terms used to describe city centers, such as Downtown, are key concepts in everyday or vernacular language. Here, we explore such language by harvesting georeferenced and tagged metadata associated with 8 million Flickr images and thus consider how large numbers of people name city core areas. The nature of errors and imprecision in tagging and georeferencing are quantified, and automatically generated precision measures appear to mirror errors in the positioning of images. Users seek to ascribe appropriate semantics to images, though bulk-uploading and bulk-tagging may introduce bias. Between 0.5–2 of tags associated with georeferenced images analyzed describe city core areas generically, while 70 of all georeferenced images analyzed include specific place name tags, with place names at the granularity of city names being by far the most common. Using Flickr metadata, it is possible not only to describe the use of the term Downtown across the USA, but also to explore the borders of city center neighborhoods at the level of individual cities, whilst accounting for bias by the use of tag profiles. The explosive growth in image sharing via social networks has produced exciting opportunities for the computer vision community in areas including face, text, product and scene recognition. In this work we turn our attention to group photos of people and ask the question: what can we determine about the social subculture or urban tribe to which these people belong? To this end, we propose a framework employing low- and mid-level features to capture the visual attributes distinctive to a variety of urban tribes. We proceed in a semi-supervised manner, employing a metric that allows us to extrapolate from a small number of pairwise image similarities to induce a set of groups that visually correspond to familiar urban tribes such as biker, hipster or goth. Automatic recognition of such information in group photos offers the potential to improve recommendation services, context sensitive advertising and other social analysis applications. We present promising preliminary experimental results that demonstrate our ability to categorize group photos in a socially meaningful manner.