Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12015/2830
Title: Social Media-Based Expertise Evidence
Authors: Yogev, Arnon
Guy, Ido
Ronen, Inbal
Zwerdling, Naama
Barnea, Maya
Issue Date: 2015
Publisher: Springer, Cham
metadata.dc.relation.ispartof: ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work
metadata.mci.reference.pages: 63-82
Series/Report no.: ECSCW
Abstract: Social media provides a fertile ground for expertise location. The public nature of the data supports expertise inference with little privacy infringement and, in addition, presentation of direct and detailed evidence for an expert’s skillfulness in the queried topic. In this work, we study the use of social media for expertise evidence. We conducted two user surveys of enterprise social media users within a large global organization, in which participants were asked to rate anonymous experts based on artificial and real evidence originating from different types of social media data. Our results indicate that the social media data types perceived most convincing as evidence are not necessarily the ones from which expertise can be inferred most precisely or effectively. We describe these results in detail and discuss implications for designers and architects of expertise location systems.
metadata.dc.identifier.doi: 10.1007/978-3-319-20499-4_4
URI: https://hdl.handle.net/20.500.12015/2830
ISBN: 978-3-319-20498-7
metadata.mci.conference.sessiontitle: Full Papers
metadata.mci.conference.location: Oslo, Norway
metadata.mci.conference.date: 19-23 September 2015
Appears in Collections:ECSCW 2015: Proceedings of the 14th European Conference on Computer Supported Cooperative Work

Files in This Item:
File Description SizeFormat 
00444.pdf1,18 MBAdobe PDFView/Open


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