In today's age of digital multimedia deluge, a clear understanding of the dynamics of online communities is capital. Users have abandoned their role of passive consumers and are now the driving force behind large-scale media repositories, whose dynamics and shaping factors are not yet fully understood. In this paper we present a novel human-centered analysis of two major photo sharing websites, Flickr and Kodak Gallery. On a combined dataset of over 5 million tagged photos, we investigate fundamental differences and similarities at the level of tag usage and propose a joint probabilistic topic model to provide further insight into semantic differences between the two communities. Our results show that the effects of the users' motivations and needs can be strongly observed in this large-scale data, in the form of what we call Kodak Moments and Flickr Diamonds. They are an indication that system designers should carefully take into account the target audience and its needs.

With the increased presence of digital imaging devices, there also came an explosion in the amount of multimedia content available online. Users have transformed from passive consumers of media into content creators and have started organizing themselves in and around online communities. Flickr has more than 30 million users and over 3 billion photos, and many of them are tagged and public. One very important aspect in Flickr is the ability of users to organize in self-managed communities called groups. This paper examines an unexplored problem, which is jointly analyzing Flickr groups and users. We show that although users and groups are conceptually different, in practice they can be represented in a similar way via a bag-of-tags derived from their photos, which is amenable for probabilistic topic modeling. We then propose a probabilistic topic model representation learned in an unsupervised manner that allows the discovery of similar users and groups beyond direct tag-based strategies, and we demonstrate that higher-level information such as topics of interest are a viable alternative. On a dataset containing users of 10 000 Flickr groups and over 1 milion photos, we show how this common topic-based representation allows for a novel analysis of the groups-users Flickr ecosystem, which results into new insights about the structure of the entities in this social media source. We demonstrate novel practical applications of our topic-based representation, such as similarity-based exploration of entities, or single and multi-topic tag-based search, which address current limitations in the ways Flickr is used today.

The amount of multimedia content available online constantly increases, and this leads to problems for users who search for content or similar communities. Users in Flickr often self-organize in user communities through Flickr Groups. These groups are particularly interesting as they are a natural instantiation of the content~+~relations social media paradigm. We propose a novel approach to group searching through hypergroup discovery. Starting from roughly 11,000 Flickr groups' content and membership information, we create three different bag-of-word representations for groups, on which we learn probabilistic topic models. Finally, we cast the hypergroup discovery as a clustering problem that is solved via probabilistic affinity propagation. We show that hypergroups so found are generally consistent and can be described through topic-based and similarity-based measures. Our proposed solution could be relatively easily implemented as an application to enrich Flickr's traditional group search.

With the increased presence of digital imaging devices there also came an explosion in the amount of multimedia content available online. Users have transformed from passive consumers of media into content creators. Flickr.com is such an example of an online community, with over 2 billion photos (and more recently, videos as well), most of which are publicly available. The user interaction with the system also provides a plethora of metadata associated with this content, and in particular tags. One very important aspect in Flickr is the ability of users to organize in self-managed communities called groups. Although users and groups are conceptually different, in practice they can be represented in the same way: a bag-of-tags, which is amenable for probabilistic topic modeling. We present a topic-based approach to represent Flickr users and groups and demonstrate it with a web application, Topickr, that allows similarity based exploration of Flickr entities using their topic-based representation, learned in an unsupervised manner.