The document presents a taxonomy for defining and classifying the Maker Movement. It uses an empirical approach, analyzing social media, Google trends, and Wikipedia statistics to inform the taxonomic structure. The analysis finds that external events can highly impact the data. It also identifies risks in using social media for taxonomy including short time spans of data, reflecting semantics between terms, and lack of testing for data generation. The document proposes drafting a taxonomic structure and integrating a time dimension to indicate increasing and decreasing terms over time.

1. An empirically informed
taxonomy for the Maker Movement
Chris&an Voigt (ZSI), Calkin Suero Montero(UEF),
Massimo Menichinelli (FabLab-BCN, AU)
Centre for Social Innova9on | Dr. Chris&an Voigt
3rd International Conference on
Internet Science INSCI 2016 –
September 12-14, Florence, Italy

2. Context:
Collec9ve Awareness Pla>orm
• The Maker Movement is -among many things - a way to
empower people to take ownership of designs and means of
produc7on.
• Ownership also means controlling value preferences
embedded within a product
• For example
– materials used,
– features provided,
– energy consumed,
– openly designed etc.
Centre for Social Innova&on |
voigt.cmc@gmail.com
An empirically informed taxonomy 2
What defines the
Maker Movement ?
Taxonomy
• comparability
• track changes
in meaning
• fostering
abstractions

3. Problem Statement & Approach
• Taxonomies are frequently ad hoc crea&ons, i.e
– unclear origins or theore&cal frameworks
– may lack empirical founda&ons
• Approach tested
– Can social media, google trends and access sta7s7cs
inform taxonomic structures? => explanatory usefullness
• Objec&ve
– Test tools, methods and risks (modera&ng effects)
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voigt.cmc@gmail.com
An empirically informed taxonomy 3

4. Taxonomy refinement
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An empirically informed taxonomy 4

5. 1ST STEP: SELECTORS
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voigt.cmc@gmail.com
An empirically informed taxonomy 5

6. Seeds
ü people: maker, hacker;
ü places: makerspaces, hackerspaces, fablabs;
ü ac9vi9es: DIY, 3d-prin&ng, making, hacking
maker_educa&on.
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voigt.cmc@gmail.com
An empirically informed taxonomy 6
13.Ratto, M.: Critical making: Conceptual and material studies in
technology and social life. The Information Society. 27, 252–260 (2011).
14.Anderson, C.: Makers: the new industrial revolution. NY (2012).
15.Dougherty, D.: The maker movement. innovations. 7, 11–14 (2012).
… used as hashtags, search terms
and wikipedia pages – ‘seed word’
concept.

7. 2ND STEP: SOCIAL MEDIA ANALYSIS
Co-Word Analysis
Wikipedia Sta&s&cs
Google Trends (Search, Co-Search)
Emo&onal Profiling
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voigt.cmc@gmail.com
An empirically informed taxonomy 7

8. Co-Word analysis in R
• download tweets
(specified by one or more hashtags, max 13.000, within last 7 days)
• create corpus (i.e. combina&on of several text files)
• clean text (only alphabe&c chars, no numbers or brackets, no URL links or
retweet– RT – or @names)
• create term matrix (word by word matrix => gets huge)
• remove sparse terms (i.e. very few combina&ons)
• plot top 20 or top 30 co-words
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voigt.cmc@gmail.com
An empirically informed taxonomy 8
needed <- c ("twitteR", "tm”, "ggplot2”)
sapply (needed, require)

9. 13,000 tweets with #3dprin9ng
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voigt.cmc@gmail.com
An empirically informed taxonomy 9
Risk #1: Short time span & high impact events

10. Centre for Social Innova&on |
voigt.cmc@gmail.com
An empirically informed taxonomy 10

11. Centre for Social Innova&on |
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An empirically informed taxonomy 11
Wikipedia Access Sta9s9cs
Another example
for the impact of
external events ...

12. Timeline interpreta9ons
• 'hackerspace’ are declining aler 2013
• visits to 'maker_culture' page are increasing
• 2013: publica&on of Chris Anderson's
Makers: The New Industrial Revolu7on.
• 2011 cover story from the Economist
"Print me a Stradivarius"
• no clear idea for decline of DIY aler 2013
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voigt.cmc@gmail.com
An empirically informed taxonomy 12

13. Google Trending
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An empirically informed taxonomy 13
Risk #2: Reflecting semantics (3D printing being part of DIY)
Risk #3: Non-testable data generation

14. 3RD STEP:
DRAFT TAXONOMIC STRUCTURE
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An empirically informed taxonomy 14

15. People:
DraX Taxonomic Structure
... so far co-word analysis, seemed the most
comprehensible approach.
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voigt.cmc@gmail.com
An empirically informed taxonomy 15

16. Outlook
• integra&on of &me dimension, indica&ng
decreasing and increasing terms (e.g. Google
Trending)
• tes&ng of automated taxonomy buidling (e.g.
in an educa&onal context where taxonomies
maner to support the exchange of materials
and lesson plans)
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voigt.cmc@gmail.com
An empirically informed taxonomy 16

17. BACK-UP
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An empirically informed taxonomy 17

18. EMOTION ANALYSIS
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An empirically informed taxonomy 18

19. Posi9ve emo9ons
– Clear: 'Lots of great prac&cal &ps for crea&ng a
library makerspace' (Eagerness)
– Ambiguous: 'Huge thanks to Cargill Salt for
approving our grant for our Maker-space. You're
helping to foster crea&vity and
innova&on!' (Liking)
– False: 'Aleph objects opens new fulfilment center
in Australia, offers free shipping - 3dprint.com
' (Fulfillment)
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voigt.cmc@gmail.com
An empirically informed taxonomy 19

20. Nega9ve emo9ons
– Clear: 'thanks! I want to try... afraid I'll do most of
the work in the minimal maker-space &me we
have!' (Distress)
– Ambiguous: 'Worried about #makerspace
logis&cs? #fallcue is here to help! It's not just for
tech! ' (Distress)
– False: 'If kids can imagine it, they can build it!
Perfect for blas&ng away boredom
#makerspace' (Weariness)
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voigt.cmc@gmail.com
An empirically informed taxonomy 20
Risk #4: Unknown classification errors