Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/12919
Full metadata record
DC FieldValueLanguage
dc.contributor.authorBrooker, P-
dc.contributor.authorBarnett, J-
dc.contributor.authorCribbin, TF-
dc.date.accessioned2016-07-08T11:39:07Z-
dc.date.available2016-07-08T11:39:07Z-
dc.date.issued2016-
dc.identifier.citationBig Data & Societyen_US
dc.identifier.issn2053-9517-
dc.identifier.urihttp://bds.sagepub.com/-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12919-
dc.identifier.urihttp://bds.sagepub.com/content/3/2/2053951716658060-
dc.description.abstract'The era of Big Data has begun' (boyd and Crawford, 2012: 662). In the few years since this statement, social media analytics has begun to accumulate studies drawing on social media as a resource and tool for research work. Yet, there has been relatively little attention paid to the development of methodologies for handling this kind of data. The few works that exist in this area often reflect upon the implications of 'grand' social science methodological concepts for new social media research (i.e. they focus on general issues such as sampling, data validity, ethics, etc). By contrast, we advance an abductively-oriented methodological suite designed to explore the construction of phenomena played out through social media. To do this, we use a software tool - Chorus - to illustrate a visual analytic approach to data. Informed by visual analytic principles, we posit a two-by-two methodological model of social media analytics, combining two data collection strategies with two analytic modes. We go on to demonstrate each of these four approaches ‘in action’, to help clarify how and why they might be used to address various research questions.en_US
dc.language.isoenen_US
dc.publisherSAGE Publishingen_US
dc.subjectSocial mediaen_US
dc.subjectTwitteren_US
dc.subjectAnalyticsen_US
dc.subjectDigital social scienceen_US
dc.subjectData visualisationen_US
dc.subjectMethodsen_US
dc.titleDoing social media analyticsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1177/2053951716658060-
dc.relation.isPartOfBig Data & Society-
pubs.publication-statusAccepted-
Appears in Collections:Dept of Computer Science Research Papers

Files in This Item:
File Description SizeFormat 
Fulltext.pdf690.07 kBAdobe PDFView/Open


Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.