Metrics for Analyzing Social Documents to Understand Joint Work

dc.contributor.authorSchubert, Petra
dc.contributor.authorMosen, Julian
dc.contributor.authorSchwade, Florian
dc.date.accessioned2020-01-04T07:11:01Z
dc.date.available2020-01-04T07:11:01Z
dc.date.issued2020-01-07
dc.description.abstractSocial Collaboration Analytics (SCA) aims at measuring and interpreting communication and joint work on collaboration platforms and is a relatively new topic in the discipline of Information Systems. Previous applications of SCA are largely based on transactional data (event logs). In this paper, we propose a novel approach for the examination of collaboration based on the structure of social documents. Guided by the ontology for social business documents (SocDOnt) we develop metrics to measure collaboration around documents that provide traces of collaborative activity. For the evaluation, we apply these metrics to a large-scale collaboration platform. The findings show that group workspaces that support the same use case are characterized by a similar richness of their social documents (i.e. the number of components and contributing authors). We also show typical differences in the “collaborativity” of functional modules (containers).
dc.format.extent10 pages
dc.identifier.doihttps://doi.org/10.24251/HICSS.2020.030
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63769
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCollaboration for Data Science
dc.subjectenterprise collaboration platform
dc.subjectenterprise social software
dc.subjectmetrics
dc.subjectsocial collaboration analytics
dc.subjectsocial documents
dc.titleMetrics for Analyzing Social Documents to Understand Joint Work
dc.typeConference Paper
dc.type.dcmiText

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