Categorizing events using spatio-temporal and user features from Flickr

Van Canneyt, Steven, Schockaert, Steven ORCID: https://orcid.org/0000-0002-9256-2881 and Dhoedt, Bart 2016. Categorizing events using spatio-temporal and user features from Flickr. Information Sciences 328 , pp. 76-96. 10.1016/j.ins.2015.08.032

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Abstract

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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Elsevier
ISSN:	0020-0255
Date of Acceptance:	21 August 2015
Last Modified:	31 Oct 2022 10:25
URI:	https://orca.cardiff.ac.uk/id/eprint/84838

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