Topic Modeling for Makerspace Artifact Analysis - David Wilson; Johanna Okerlund; Dominique Exley (Proceedings of the International Florida Artificial Intelligence Research Society Conference)
As the making phenomenon becomes more prevalent, diverse, and vast, it becomes increasingly challenging to identify general temporal or spatial trends in types of making endeavors. Identifying trends in what participants are making is important to makerspace leaders who seek to understand the impact of the making phenomenon on the world or who are interested in broadening participation within their own maker contexts. This paper shows how topic modeling by means of LDA can be used to analyze maker artifacts, and illustrates how these types of insights can be used to make inferences about the making phenomenon, as well as to inform efforts to broaden participation.