The Maker Movement emerged from a renewed interest in the physical side of innovation and the decreasing costs of many digital fabrication technologies. Classifying the terminology used to describe the fundamental activities in making is the purpose of analyzing data from Google Search, Twitter and Wikipedia.

Google search and Twitter co-word analysis support the identification of related concepts - the basic building blocks for describing the maker movement. Data from Google Trends and Wikipedia Access Statistics are more suitable to support the narratives around the identified concepts (e.g. how their popularity changed over time or whether they occur in a primarily positive or negative context). What is still missing is a technique that can aggregate single concepts into broader, bottom-up categories.

Taking advantage of the growth of the Social Web and participation platforms, we analyze a number of sources such as Twitter, Wikipedia and Google Trends. Analytically we apply co-word analysis, trend visualizations and emotional analysis. Whereas co-words and trends extract structural characteristics of the movement, emotional analysis is non-topical, extracting emotional interpretations. Practically we distinguish between concept identification (seeds) and concept behaviour over time (trends). Changes over time strongly depend on large-scale, longitudinal data in order to mitigate the impact of single events (e.g. product launches, controversial statements), which demonstrate the living spirit if a community but do not necessarily reflect the distribution of interests 'on average'. A more in-depth description of the research can be found in this related publications:

Voigt, C., Montero, C. S., & Menichinelli, M. (2016). An empirically informed  taxonomy for the Maker movement. In International conference on Internet Science (INSCI  2016). Florence, Italy: Springer. DOI: 10.1007/978-3-319-45982-0_17