A Survey on Visual Analytics of Social Media Data


Dateien zu dieser Ressource

Dateien Größe Format Anzeige

Zu diesem Dokument gibt es keine Dateien.

WU, Yingcai, Nan CAO, David GOTZ, Yap-Peng TAN, Daniel A. KEIM, 2016. A Survey on Visual Analytics of Social Media Data. In: IEEE Transactions on Multimedia. 18(11), pp. 2135-2148. ISSN 1520-9210. eISSN 1941-0077

@article{Wu2016Surve-37784, title={A Survey on Visual Analytics of Social Media Data}, year={2016}, doi={10.1109/TMM.2016.2614220}, number={11}, volume={18}, issn={1520-9210}, journal={IEEE Transactions on Multimedia}, pages={2135--2148}, author={Wu, Yingcai and Cao, Nan and Gotz, David and Tan, Yap-Peng and Keim, Daniel A.} }

<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/37784"> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/37784"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-02-28T16:32:06Z</dc:date> <dc:creator>Wu, Yingcai</dc:creator> <dc:creator>Gotz, David</dc:creator> <dc:creator>Cao, Nan</dc:creator> <dc:creator>Tan, Yap-Peng</dc:creator> <dc:contributor>Cao, Nan</dc:contributor> <dc:contributor>Gotz, David</dc:contributor> <dcterms:issued>2016</dcterms:issued> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2017-02-28T16:32:06Z</dcterms:available> <dc:contributor>Wu, Yingcai</dc:contributor> <dc:contributor>Tan, Yap-Peng</dc:contributor> <dc:language>eng</dc:language> <dcterms:abstract xml:lang="eng">The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, has received considerable attention in recent years. Many visual analytics methods have been proposed across disciplines to understand large-scale structured and unstructured social media data. This objective, however, also poses significant challenges for researchers to obtain a comprehensive picture of the area, understand research challenges, and develop new techniques. In this paper, we present a comprehensive survey to characterize this fast-growing area and summarize the state-of-the-art techniques for analyzing social media data. In particular, we classify existing techniques into two categories: gathering information and understanding user behaviors. We aim to provide a clear overview of the research area through the established taxonomy. We then explore the design space and identify the research trends. Finally, we discuss challenges and open questions for future studies.</dcterms:abstract> <dc:contributor>Keim, Daniel A.</dc:contributor> <dcterms:title>A Survey on Visual Analytics of Social Media Data</dcterms:title> <dc:creator>Keim, Daniel A.</dc:creator> </rdf:Description> </rdf:RDF>

Das Dokument erscheint in:

KOPS Suche


Mein Benutzerkonto