In the scholarly community, the concept of complexity is typically associated with science. For example, the Wikipedia page on complexity mentions six aspects of complexity (behavior, mechanisms, simulation, data, and molecular recognition), all of which are science-related. However, the consumer view on complexity is rather different. To understand the conceptual structure of complexity, as seen by laypersons, we analyzed the self-declared interests (keywords) harvested from complexity-related blogging communities in LiveJournal – a popular blogging and social networking web site. We arranged the most frequently declared 374 interests into a semantic network, based on their similarity of use. The resulting semantic network (see attachment) has a clear modular structure with the modularity m=0.56. It consists of three larger clusters of approximately equal size and a smaller cluster, linked with the four consumer-related aspects of complexity: Science (major keywords: mathematics, artificial intelligence, complexity theory, evolution, science, psychology, physics, chaos, singularities, logic), Philosophy (philosophy, self-expression, imagination, knowledge, wisdom, awareness, learning, dreams, nature, ideas), Art (art, writing, books, romance, drawing, photography, comics, literature, fashion, education), and Soul (complexity, simplicity, music, creativity, truth, love, beauty, honesty, poetry, intelligence). The node size corresponds to the term use frequency, and the aggregate frequency of the terms in the non-science-related clusters is overwhelming. We hypothesize that laypersons, who are not involved in complexity research, perceive complexity not only, and not in the first place, as a scientific phenomenon, but as an intricacy associated with creativity, search for wisdom, and soul search. Furthermore, we observe that the soul searching complexity is recognized as potentially physically painful, as exemplified by the sizable subcomponent of the Soul cluster, pertinent to self-harm and suicide (major keywords: pain, drugs, depression, suicidal thoughts, nightmares, fear, self-injury). The technique of decomposing a complex concept into semantic clusters can be applied to a broad range of vaguely defined terms.

1. 1
The Pain of Complexity
Dmitry Zinoviev
Suffolk University, Boston

2. 2
“Complexity” is one of those fuzzy concepts
that everyone seems to understand
differently. Other examples: sustainability,
resilience, success...
What Is Complexity?

3. 3
Let's Ask the Mechanical Turk!
● Question: “Enter three nouns
(please, nouns only!) that, to
the best of your understanding,
are associated with complexity.
These words could be
synonyms, descriptors,
attributes of complexity, actions
or any other related words.”
● Ask 100 mTurk workers, pay a
nominal fee for an answer
● Reject non-nouns; stem* and
aggregate nouns
*We tried both “Porter” and “Lancaster” stemmers; “Porter” is less
aggressive, it produced more stems (“motifs”); some manual tweaking
was needed

4. 4
Direct Survey Results
● The stems that were mentioned at least twice (in the order of
decreasing frequency):
– Most frequently mentioned (23+ times): COMPLIC*, DIFFICULTI*,
INTRICACI*
– Less frequently mentioned (2–9 times): PROBLEM*, ELABOR*,
ENTANGL*, PUZZL*, MULTIPL*, COMPLEX*, COMPUT*,
CONVOLUT*, HARD*, RAMIF*, SOPHIST*, CHALLENG*,
HUMAN*, INVOLV*, LIFE*, POLIT*, RIDDL*, SCIENC*,
COMPOSIT*, CONFUS*, DEPTH*, DESIGN*, DIVERS*,
LABYRINTH*, LANGUAG*, MATH*, MAZE*, OBSCUR*,
PHYSIC*, QUALITI*, RELATIONSHIP*, SPACE*,
STRUCTURE*, TEST*, TRICKI*, TROUBL*, VARIABL*,
VARIETI*
– The highlighted stems bear strong emotional load—though, no pain
yet!

5. 5
Use Indirect Data
● Don't tell me who you are. “Tell me who your friends are, and I will tell
you who you are.”
● Instead of asking people what they think “complexity” is, let's collect
abundant implicit markers off the Internet.

6. 6
Indirect Data: Possible Sources
● EBSCO keywords or subject tags (subscription and harvesting
software required; we haz it, but the results may be too academic!)
● Blogging sites like LiveJournal (LJ)
– LJ has individual blogs and community blogs (like forums)
– Both types of blogs can and do have user- or moderator-declared
interests
– The interests are usually carefully chosen to reflect the
blogger's/community's online identity
– Free access, easy-to-write harvester

7. 7
Raw Data
● All LiveJournal communities that list at least one of the following
interests:
– “complexity”
– “complex systems”
– “complexity theory”
– “complexity theories”
● Found 59 communities, such as mixedtype, abstractthought,
ivygreenfanclub, pdmi_logic, investors, gifted_teens, etc.
● Many more individual bloggers—we ignored them, their interest lists
are way too broad!
● Selected 374 most frequently declared interests (the corpus)

8. 8
Example: “Ecologists” community
● Community ecologists.livejournal.com
● 150 self-declared (by the moderator(s)) interests
● Includes “complexity,” but also many other keywords; are
they relevant? Let's assume they are.

9. 9
Term Vector Model
● Treat communities as documents and interests as words
● Calculate generalized similarity between words (and between
communities—but why?) using Kovacs (2010) algorithm:
– Two words are similar if they belong to similar documents
– Two documents are similar if the consist of similar words
● Remark: generalized similarity, as defined by Kovacs, is essentially
Pearson correlation in an oblique Cartesian coordinate space
●
For any two words W and V from the corpus, -1 ≤ d(W,V) ≤ 1 is the
similarity between the words; for any two communities C and D, -1 ≤
d(C,D) ≤ 1 is the similarity between the communities (a by-product,
not needed)

10. 10
Network Construction (1)
● Treat words as nodes in a
network
● Treat similarities as edges if
they are above T0
=0.65
(“slicing”); the choice of T0
is
tricky and arbitrary:
– If T0
is too high, the
network is complete
and not interesting
– If T0
is too low, the
network is sparse and
disconnected
● Gephi it!

11. 11
“Complexity Dragon” (a.k.a. the
Mindscape of Complexity)

12. 12
Modular Structure
Modularity=0.56
(not great, but
still visible)
psychology, chaos,
science, mathematics,
complexity theory, ...
philosophy, life,
imagination, self-expression,
knowledge, ...
complexity, honesty, music, simplicity,
love, poetry, creativity,
empathy, humor, hate, ...
writing, art, books,
romance, drawing, ...

13. 13
What Is the Meaning of Modules?
● We could just stare at the
modules and try to come up
with a suitable name—
● —or we could use
crowdsourcing through Amazon
Mechanical Turk again!
Fragment of the “fireball”

14. 14
Let's Ask the Mechanical Turk!
● Question: “Describe the
following group of 50* words
with a single most suitable word
or a two-word or three-word
phrase: ...”
*There are only 36 terms in the lower
left module.
● Ask 100 mTurk workers, pay a
nominal fee for an answer
● Reject non-nouns; stem and
aggregate nouns
● The nouns are module
descriptors (“motifs”)

15. 15
Network Construction (2)
● Treat original modules as documents and motifs as words
● Calculate generalized similarity
● Do slicing
● Build a network of stems
● Gephi it!

16. 16
The Three Shades of Complexity
● The new network has a simple,
but not trivial, structure, and is
highly modular
● The Three Shades of
Complexity:
– Science/Technology
– Society/Mind
– Creativity/Emotions
● Connections:
– via education
– via life/humanities
There are lines here, they
are just too thin...

17. 17
Semantic Spectrography
mTurk for motif
extraction and clustering
Term source for
mindscape construction
Analysis term
selection

18. 18
Spectrography vs Direct Survey
Direct Survey: 37
Spectrography: 382
4 9 4
104 111 113 37
24

19. 19
Comparison Summary
● Positive:
– Spectrography is much more detailed: it catches 41% of direct
survey terms; direct survey catches only 4.5% of spectrography
terms
– Spectrography reveals structure
● Negative:
– Spectrography requires a source (or sources) of terms

20. 20
Successfully Used Elsewhere
● D. Zinoviev, D. Stefanescu, L. Swenson, and G. Fireman, “Semantic
Networks of Interests in Online NSSI* Communities,” in Proc.
Workshop “Words and Networks,” Evanston, IL, June 2012,
published online (also submitted to Social Networks in 2013)
● D. Zinoviev and Z. Zhu, “Conceptual Structure of Sustainability:
Social and Scholarly Perspectives,” Sunbelt XXXIV Social Networks
Conference, St.-Pete Beach, FL, February 2014 (also submitted to
Social Networks in 2014)
*Non-Suicidal Self Injury—a common and
epidemically spreading activity among adolescents
and young adults

21. 21
But Where Is the Pain?
● SE corner of the emotional module full of NSSI-specific terminology
● Communities: artificial_joy, humans_being (sic), the_addicted
● Complexity and creativity as attributes of human nature

22. 22
Thank-You!