Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices
Association for Computing machinery (ACM)
GROUP '20: Companion of the 2020 ACM International Conference on Supporting Group Work
Social 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.
Kogan, Marina; Halfaker, Aaron; Guha, Shion; Aragon, Cecelia; Muller, Michael; and Geiger, Stuart, "Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices" (2020). Computer Science Faculty Research and Publications. 66.