Analyzing people is great -- we can just talk to them -- but hard: their answers are fuzzy. This talk walks through our analysis of a Twitter botnet and another of how programmers pick programming languages. Interestingly, we used interactive graph visualizations to unravel mysteries in both.

1. Visually Analyzing People
Leo Meyerovich (@LMeyerov)
CEO 1

2. is:
Supercharging visual analytics
through GPU cloud streaming.
(We tricky graphs.)

4. CASE STUDY:
TWITTER FRAUD
Naïve layout on 1K+ node graphs
give impenetrable hairballs.
Gauss-Seidel Force-Directed Graph, O(N^2) n-body, GPU
Node: Twitter account
Edge: Friendship
Friends and friend-of-friends of a bot
who randomly messaged real
people and retweeted them.

5. Even on a small graph (77 nodes),
smart design starts adding clarity

6. With smart layouts, fake account clusters pop out
ForceAtlas2 Layout, O(n log n) n-body, GPU
The spambot
is an entrypoint
to more bots…
Obviously fake
account names

7. A quiet small business who buys
virtual game currency from gamers…

8. Who somehow got exactly
1 message massively
trended & advertised by Twitter

9. spammer
laundering
accounts
bot retweet network
It’s a “retweet laundering” botnet!
Tricks Twitter into targeting gamers
to check out a cyberfraud site.
They steal gamers’ money and identities.

10. Relationships hard to see without
graphs with smart layouts & interactions.
Next step: explore the time dimension
Ex: how do mobs launch from Twitter?

11. Leo A. Meyerovich, @lmeyerov,
Graphistry
THE
SOCIOLOGY
OF
PROGRAMMING
LANGUAGES
11

12. http://hammerprinciple.com/therighttool

13. ~14,000
developers

14. Fastest? C > Java> JavaScript > Pascal
Safest? Java > Pascal > JavaScript > C
Goal: Rank Beliefs
Programmers won’t
agree on ranking..

15. Idea: Chess Ranking

16. Let’s run a competition for the
friendliest language! (Glicko2)
Each survey response is a game match:
1. Person A says Python beats C in friendliness
2. Person A says Java beats C in friendliness
3. Person B says C beats APL in friendliness
…

17. Score Points set by a Bookie
Every language starts with rank 1000
1. “Person A: Python friendlier than C”
 Python’s rank goes up
2. “Person B: Python friendlier than C”
 Python already > C, less valuable win
3. “Person C: Haskell friendlier than Python”
Problem: little known about Haskell (“sparse”)
 Haskell beat a high-rank language: big level increase!
(Bayesian!)

18. Many Tournaments = Correlation Matrix!
Language x Belief

19. Cluster (K-Means)

20. Reduce Dimensionality:
Pick fun languages & cluster centers

21. Graphs are (Adjacency)
Matrices

22. Correlation Matrices are Fuzzy Graphs
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5 0.5 0.5
0.5
0.5
0.5 0.5
0.5 0.5

23. Weak Edges Are Annoying!

24. Filter: Only Show Strong Relationships

25. Relationships hard to see without
graphs with smart layouts & interactions.
Step 2 of analysis is correlate (step 1 is count).
Correlations are relationships,
so explore them as graphs!

26. Projects (2000-2010)200K
[PLATEAU 2013]
26

27. 0%
20%
40%
60%
Popularity
Project categories (223)
Java
0%
1%
2%
3%
4%
Project categories (223)
Scheme
Popularity Across Niches
27
bloggin
g
searc
h
build tools

28. Prolog
VBScript Scheme
Fortran
PL/SQL Assembly
C#
Java
0.0001
0.001
0.01
0.1
1
0 1 2 3 4 5
Popularity
Dispersion across niches
(σ / μ)
Popularity vs. Niche: Dispersion
28

29. 0.0100%
0.1000%
1.0000%
10.0000%
100.0000%
1 10 100
Proportion
of
Projects for
Language
Language Rank (Decreasing )
Language Use (survey)
29
Java: winner takes all
Long Tail
Design for
niches
and grow 

30. Survey of 1,679 Developers
Extrinsic factors
dominate!
(on last
project)
30

31. FUTURE STEP:
Now that we’ve counted things, let’s correlate them!
Topics in Free-form ResponsesAnswer Correlations

32. Relationships hard to see without
graphs with smart layouts & interactions.
Step 2 of analysis is correlate (step 1 is count).
Correlations are relationships,
so explore them as graphs!
Powerful because correlations everywhere:
raw features, inferred topics, …

33. We’re Hiring Designers!
(and contact if you have interesting
graphs)
33
info@graphistry.com
Twitter: @LMeyerov