Modeling flickr communities through probabilistic topic-based analysis

Abstract—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 repre-sentation 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. Index Terms—Flickr, social media, Probabilistic Topic Models. I