Categorizing events using spatio-temporal and user features from Flickr
Even though the problem of event detection from social media has been well studied in recent years, few authors have looked at deriving structured representations for their detected events. We envision the use of social media for extracting large-scale structured event databases, which could in turn be used for answering complex (historical) queries. As a key stepping-stone towards this goal, we introduce a method for discovering the semantic type of extracted events, focusing in particular on how this type is influenced by the spatio-temporal grounding of the event, the profile of its attendees, and the semantic type of the venue and other entities which are associated with the event. We estimate the aforementioned characteristics from metadata associated with Flickr photos of the event and then use an ensemble learner to identify its most likely semantic type. Experimental results based on an event dataset from Upcoming.org and Last.fm show a marked improvement over bag-of-words based methods.
events flickr social media ensemble learning semi-structured data