Characterizing user-subgroups in Flickr Group: A block LDA based approach

The last few years have seen an exponential increase in the amount of multimedia content that is available online thanks to collaborative-online communities such as Flickr, You Tube etc. As opposed to “pure” social networking services these collaborative-online communities not only allow users to create new social links (e.g. add people to one's friend list) but also allow users to contribute multimedia content and engage in content-driven interactions. A good example of this can be seen in Flickr, in general and Flickr Group in particular where users can comment on or “like” an image contributed by another user. This paper looks at utilizing this within group user-user interaction information, along with image meta-data to discover user communities (user-subgroups) that contribute content around specific topics (subgroup-themes) at specific points in time. A good example of this is a group of users (e.g sports fans) contributing content and interacting with each other only at specific times of the year (e.g close to their favorite sporting event). We demonstrate that our proposed generative model Temporal BlockLink LDA is able to successfully extract such user-subgroups, subgroup-themes and associated temporal patterns from data in an unsupervised manner.