Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation.
New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking in...
| Publicado en: | Sociology Vol. 51; no. 6; pp. 1149 - 1169 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Sage Publications Inc.
Dec2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=126598020&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 126598020 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00380385 SGY jtl: Sociology issn: 00380385 maglogo: Y pubinfo: dt: Dec2017 vid: 51 iid: 6 pid: 344 pub: Sage Publications Inc. artinfo: ui: 126598020 10.1177/0038038517708140 ppf: 1149 ppct: 20 formats: tig: atl: Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation. aug: au: Williams, Matthew L. Burnap, Pete Sloan, Luke affil: Cardiff University, UK su: Twitter (Web resource) Social media Sociologists Social sciences Algorithms sug: subj: Social media Sociologists Social sciences Research and Development in the Social Sciences and Humanities Algorithms Twitter (Web resource) keyword: algorithms computational social science context collapse ethics social data science social media algorithms computational social science context collapse ethics social data science social media ab: New and emerging forms of data, including posts harvested from social media sites such as Twitter, have become part of the sociologist’s data diet. In particular, some researchers see an advantage in the perceived ‘public’ nature of Twitter posts, representing them in publications without seeking informed consent. While such practice may not be at odds with Twitter’s terms of service, we argue there is a need to interpret these through the lens of social science research methods that imply a more reflexive ethical approach than provided in ‘legal’ accounts of the permissible use of these data in research publications. To challenge some existing practice in Twitter-based research, this article brings to the fore: (1) views of Twitter users through analysis of online survey data; (2) the effect of context collapse and online disinhibition on the behaviours of users; and (3) the publication of identifiable sensitive classifications derived from algorithms. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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