ABSTRACT The Do-It-Yourself (DIY) community is currently one of the
largest creative content communities on Pinterest (Hall et al., 2018), a social networking
service (SNS) that encourages users to both share information about creative processes and
attempt projects in real life (IRL). Pinterest users share ongoing projects by creating
Project “Pins”, which consist of images, videos, and text descriptions of creative content.
And yet, while several studies have investigated user behavior in relation to everyday
ideation and creativity on the site (Linder et al., 2014, Hu et al., 2018, Mull and Lee,
2014), little is known about the characteristics that lead users to prefer some DIY projects
over others. Thus, this paper introduces the Pinterest-DIY data set, which consists of text
data mined from 500 DIY project Pins on Pinterest. Using a custom sampling approach, we
created a taxonomy of DIY characteristics related to each Pin’s project type, function,
materials, and complexity. To measure user preferences on the site, we also conducted a
sentiment analysis on user comments for each DIY project Pin. This paper introduces the data
set and presents two use cases for the internet research community using both exploratory
and confirmatory statistical methods. In our view, the Pinterest-DIY data set will provide
further opportunities to examine whether, and to what degree, participation in online DIY
communities promotes everyday creativity and increases engagement with physical
matter.