Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices

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dc.contributor.authorKogan, Marina
dc.contributor.authorHalfaker, Aaron
dc.contributor.authorGuha, Shion
dc.contributor.authorAragon, Cecilia
dc.contributor.authorMuller, Michael
dc.contributor.authorGeiger, Stuart
dc.date.accessioned2023-03-17T22:48:57Z
dc.date.available2023-03-17T22:48:57Z
dc.date.issued2020
dc.description.abstractSocial media platforms and social network sites generate a multitude of digital trace behavioral data, the scale of which often necessitates the use of computational data science methods. On the other hand, the socio-behavioral and often relational nature of the social media data requires the attention to context of user activity traditionally associated with qualitative analysis. Human-Centered Data Science (HCDS) attempts to bridge this gap by both harnessing the power of computational techniques and accounting for highly situated and nuanced nature of the social media activity. In this workshop we plan to consider the methods, pitfalls, and approaches of how to do HCDS effectively. Moreover, from collating and organizing these approaches we hope to progress to considering best (or at least common) practices in HCDS.en
dc.identifier.doi10.1145/3323994.3369898
dc.identifier.urihttps://dl.eusset.eu/handle/20.500.12015/4592
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.relation.ispartofCompanion Proceedings of the 2020 ACM International Conference on Supporting Group Work
dc.subjectqualitative methods
dc.subjectquantitative methods
dc.subjectsocial media data
dc.subjecthuman-centered data science
dc.titleMapping Out Human-Centered Data Science: Methods, Approaches, and Best Practicesen
dc.typeText/Conference Paper
gi.citation.startPage151–156
gi.conference.locationSanibel Island, Florida, USA

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