000150608 001__ 150608
000150608 005__ 20181203021941.0
000150608 0247_ $$2doi$$a10.1109/TMM.2010.2050649
000150608 022__ $$a1520-9210
000150608 02470 $$2ISI$$a000282306500005
000150608 037__ $$aARTICLE
000150608 245__ $$aModeling and Understanding Flickr Communities through Topic-based Analysis
000150608 260__ $$c2010
000150608 269__ $$a2010
000150608 336__ $$aJournal Articles
000150608 520__ $$aWith the increased presence of digital imaging devices there also came an explosion in the amount of multimedia content available online. Users have transformed from passive consumers of media into content creators and have started organizing themselves in and around online communities. Flickr has more than 30 million users and over 3 billion photos, and many of them are tagged and public. One very important aspect in Flickr is the ability of users to organize in self-managed communities called groups. This paper examines an unexplored problem, which is jointly analyzing Flickr groups and users. We show that although users and groups are conceptually different, in practice they can be represented in a similar way via a bag-of-tags derived from their photos, which is amenable for probabilistic topic modeling. We then propose a probabilistic topic model representation learned in an unsupervised manner that allows the discovery of similar users and groups beyond direct tag-based strategies and we demonstrate that higher-level information such as topics of interest are a viable alternative. On a dataset containing users of 10,000 Flickr groups and over 1 milion photos, we show how this common topic-based representation allows for a novel analysis of the groups-users Flickr ecosystem, which results into new insights about the structure of the entities in this social media source. We demonstrate novel practical applications of our topic-based representation, such as similarity-based exploration of entities, or single and multi-topic tag-based search, which address current limitations in the ways Flickr is used today.
000150608 6531_ $$aFlickr
000150608 6531_ $$aprobabilistic topic models
000150608 6531_ $$asocial media
000150608 6531_ $$aLatent Semantic Analysis
000150608 700__ $$0243367$$g176657$$aNegoescu, Radu-Andrei
000150608 700__ $$aGatica-Perez, Daniel$$g171600$$0241066
000150608 773__ $$j12$$tIEEE Transactions on Multimedia$$k5$$q399-416
000150608 909C0 $$xU10381$$0252189$$pLIDIAP
000150608 909CO $$pSTI$$particle$$ooai:infoscience.tind.io:150608
000150608 937__ $$aEPFL-ARTICLE-150608
000150608 970__ $$aNegoescu_IEEET-MM_2010/LIDIAP
000150608 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000150608 980__ $$aARTICLE