This paper presents a traffic characterization study of the popular video sharing service, YouTube. Over a three month period we observed almost 25 million transactions between users on an edge network and YouTube, including more than 600,000 video downloads. We also monitored the globally popular videos over this period of time. In the paper we examine usage patterns, file properties, popularity and referencing characteristics, and transfer behaviors of YouTube, and compare them to traditional Web and media streaming workload characteristics. We conclude the paper with a discussion of the implications of the observed characteristics. For example, we find that as with the traditional Web, caching could improve the end user experience, reduce network bandwidth consumption, and reduce the load on YouTube’s core server infrastructure. Unlike traditional Web caching, Web 2.0 provides additional metadata that should be exploited to improve the effectiveness of strategies like caching.

Online photo services such as Flickr and Zooomr allow users to share their photos with family, friends, and the online community at large. An important facet of these services is that users manually annotate their photos using so called tags, which describe the contents of the photo or provide additional contextual and semantical information. In this paper we investigate how we can assist users in the tagging phase. The contribution of our research is twofold. We analyse a representative snapshot of Flickr and present the results by means of a tag characterisation focussing on how users tags photos and what information is contained in the tagging. Based on this analysis, we present and evaluate tag recommendation strategies to support the user in the photo annotation task by recommending a set of tags that can be added to the photo. The results of the empirical evaluation show that we can effectively recommend relevant tags for a variety of photos with different levels of exhaustiveness of original tagging.

The advent of media-sharing sites like Flickr and YouTube has drastically increased the volume of community-contributed multimedia resources available on the web. These collections have a previously unimagined depth and breadth, and have generated new opportunities – and new challenges – to multimedia research. How do we analyze, understand and extract patterns from these new collections? How can we use these unstructured, unrestricted community contributions of media (and annotation) to generate “knowledge”? As a test case, we study Flickr – a popular photo sharing website. Flickr supports photo, time and location metadata, as well as a light-weight annotation model. We extract information from this dataset using two different approaches. First, we employ a location-driven approach to generate aggregate knowledge in the form of “representative tags ” for arbitrary areas in the world. Second, we use a tag-driven approach to automatically extract place and event semantics for Flickr tags, based on each tag’s metadata patterns. With the patterns we extract from tags and metadata, vision algorithms can be employed with greater precision. In particular, we demonstrate a location-tag-vision-based approach to retrieving images of geography-related landmarks and features from the Flickr dataset. The results suggest that community-contributed media and annotation can enhance and improve our access to multimedia resources – and our understanding of the world.

This paper presents a high-level overview of Yahoo Research Berkeley’s approach to multimedia research and the ideas motivating it. This approach is characterized primarily by a shift away from building subsystems that attempt to discover or understand the “meaning” of media content toward systems and algorithms that can usefully utilize information about how media content is being used in specific contexts; a shift from semantics to pragmatics. We believe that, at least for the domain of consumer and web videos, the latter provides a more promising basis for indexing media content in ways that satisfy user needs. To illustrate our approach, we present ongoing work on several applications which generate and utilize contextual usage meta-data to provide novel and useful media experiences.

Abstract. The continued increase in Web usage, in particular participation in folksonomies, reveals a trend towards a more dynamic and interactive Web where individuals can organise and share resources. Tagging has emerged as the de-facto standard for the organisation of such resources, providing a versatile and reactive knowledge management mechanism that users find easy to use and understand. It is common nowadays for users to have multiple profiles in various folksonomies, thus distributing their tagging activities. In this paper, we present a method for the automatic consolidation of user profiles across two popular social networking sites, and subsequent semantic modelling of their interests utilising Wikipedia as a multi-domain model. We evaluate how much can be learned from such sites, and in which domains the knowledge acquired is focussed. Results show that far richer interest profiles can be generated for users when multiple tag-clouds are combined. 1

There have been many location sharing systems developed over the past two decades, and only recently have they started to be adopted by consumers. In this paper, we present the results of three studies focusing on the foursquare check-in system. We conducted interviews and two surveys to understand, both qualitatively and quantitatively, how and why people use location sharing applications, as well as how they manage their privacy. We also document surprising uses of foursquare, and discuss implications for design of mobile social services. Author Keywords foursquare, mobile computing, social computing, check-in, privacy, location based service, uses and gratifications

While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we

The advent of media-sharing sites like Flickr has drastically increased the volume of community-contributed multimedia resources on the web. However, due to their magnitudes, these collections are increasingly difficult to understand, search and navigate. To tackle these issues, a novel search system, ContextSeer, is developed to improve search quality (by reranking) and recommend supplementary information (i.e., search-related tags and canonical images) by leveraging the rich context cues, including the visual content, high-level concept scores, time and location metadata. First, we propose an ordinal reranking algorithm to enhance the semantic coherence of text-based search result by mining contextual patterns in an unsupervised fashion. A novel feature selection method, wc-tf-idf is also developed to select informative context cues. Second, to represent the diversity of search result, we propose an efficient algorithm cannoG to select multiple canonical images without clustering. Finally, ContextSeer enhances the search experience by further recommending relevant tags. Besides being effective and unsupervised, the proposed methods are efficient and can be finished at query time, which is vital for practical online applications. To evaluate ContextSeer, we have collected 0.5 million consumer photos from Flickr and manually annotated a number of queries by pooling to form a new benchmark, Flickr550. Ordinal reranking achieves significant performance gains both in Flcikr550 and TRECVID search benchmarks. Through a subjective test, cannoG expresses its representativeness and excellence for recommending multiple canonical images.

This article presents a characterization of user behavior on Flickr, a popular on-line photo sharing service that allows users to store, search, sort and share their photos. Based on a sub-set of photos being uploaded during a 10 day window, we track the interest of users in those photos over a period of 50 days. In particular we investigate the user behavior on temporal, social, and spatial dimensions. Results show that the users are able to discover new photos within hours after being uploaded and that 50 % of the photo views are generated within the first two days. The social networking behavior of users, and photo pooling are identified as the two major indicators related to a photo’s popularity. Finally we show that the geographic distribution is more focussed around a geographic location for the infrequently viewed photos, than for the photos that attract a large number of views. 1.

In this paper we investigate generic methods for placing photos uploaded to Flickr on the World map. As primary input for our methods we use the textual annotations provided by the users to predict the single most probable location where the image was taken. Central to our approach is a language model based entirely on the annotations provided by users. We define extensions to improve over the language model using tag-based smoothing and cell-based smoothing, and leveraging spatial ambiguity. Further we demonstrate how to incorporate GeoNames 1, a large external database of locations. For varying levels of granularity, we are able to place images on a map with at least twice the precision of the state-of-the-art reported in the literature.