Review of social media network analysis of Internet social spaces like twitter, flickr, email, message boards, etc. Network analysis and visualization of social media collections of connections.

1. Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://delicious.com/marc_smith/Paper
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith
Mobile social media networks

2. Youse.Y’all.
Yes, youse.
2

3. A place apart
A part of every place
Mobile Social Software
“MoSoSo”

4. 4
Email (and more) is
from people to people

5. Patterns are left behind
5

6. When my phone notices your phone
a new set of mobile social software applications
become possible that
capture data about other people
as they beacon
their identifies to one another.

7. Interactionist
Sociology
• Central tenet
– Focus on the active effort of
accomplishing interaction
• Phenomena of interest
– Presentation of self
– Claims to membership
– Juggling multiple (conflicting) roles
– Frontstage/Backstage
– Strategic interaction
– Managing one’s own and others’ “face”
• Methods
– Ethnography and participant observation
– (Goffman, 1959; Hall, 1990)

8. Innovations in the interaction order:
45,000 years ago: Speech, body adornment
10,000 years ago: Amphitheater
5,000 years ago: Maps
150 years ago: Clock time
-2 years from now: machines with
social awareness

9. Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.

10. • Hardin, Garrett. 1968/1977. “The tragedy
of the commons.” Science 162: 1243-48.
Pp. 16-30 in Managing the Commons,
edited by G. Hardin and J. Baden. San
Francisco: Freeman.
• Wellman, Barry. 1997. “An electronic group
is virtually a social network.” In S. Kiesler
(Ed.), The Culture of the Internet. Hillsdale,
NJ: Lawrence Erlbaum.
10
Nobel in
Economics
2009

11. 11
Source: xkcd, http://xkcd.com/386/
Motivations for
contribution to
public goods

12. Social media
usage generates
Social Networks
Social media platforms
are a source of multiple
Social network data
sets:
“Friends”
“Replies”
“Follows”
“Comments”
“Reads”
“Co-edits”
“Co-mentions”
“Hybrids”

13. 13

14. 14

15. 15

16. 16

17. 17

18. 18

19. 19
Answer
Person
Signatures
Discussion
People

20. Spammer
Discussion
Starter
Reply oriented
Discussion
Flame
Warrior
20

21. 21

22. • Central tenet
– Social structure emerges from
– the aggregate of relationships (ties)
– among members of a population
• Phenomena of interest
– Emergence of cliques and clusters
– from patterns of relationships
– Centrality (core), periphery (isolates),
– betweenness
• Methods
– Surveys, interviews, observations,
log file analysis, computational
analysis of matrices
(Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)
Source: Richards, W.
(1986). The NEGOPY
network analysis
program. Burnaby, BC:
Department of
Communication, Simon
Fraser University. pp.7-
16
Social Network
Theory

23. SNA 101
• Node
– “actor” on which relationships act; 1-mode versus 2-mode networks
• Edge
– Relationship connecting nodes; can be directional
• Cohesive Sub-Group
– Well-connected group; clique; cluster
• Key Metrics
– Centrality (group or individual measure)
• Number of direct connections that individuals have with others in the group (usually look at
incoming connections only)
• Measure at the individual node or group level
– Cohesion (group measure)
• Ease with which a network can connect
• Aggregate measure of shortest path between each node pair at network level reflects
average distance
– Density (group measure)
• Robustness of the network
• Number of connections that exist in the group out of 100% possible
– Betweenness (individual measure)
• # shortest paths between each node pair that a node is on
• Measure at the individual node level
• Node roles
– Peripheral – below average centrality
– Central connector – above average centrality
– Broker – above average betweenness
E
D
F
A
CB
H
G
I
C
D
E
A B D E

24. SNA
Resources

25. The Ties that Blind?
25

26. Reply-To Network
Network at distance 2 for the most prolific author of the
microsoft.public.internetexplorer.general newsgroup
The Ties that Blind?

27. Darwin Bell
27

28. Pajek without modification can
sometimes reveal structures of great interest.
The Ties that Blind?

29. Two “answer people” with an emerging 3rd.
Mapping
Newsgroup
Social
Ties
Microsoft.public.windowsxp.server.general
29

30. 30

31. Distinguishing attributes of online social roles
• Answer person
– Outward ties to local
isolates
– Relative absence of
triangles
– Few intense ties
• Reply Magnet
– Ties from local isolates
often inward only
– Sparse, few triangles
– Few intense ties
31

32. Distinguishing attributes:
• Answer person
– Outward ties to local
isolates
– Relative absence of
triangles
– Few intense ties
• Discussion person
– Ties from local isolates
often inward only
– Dense, many triangles
– Numerous intense ties
32

33. Leading research: Adamic et al. 2008
Knowledge Sharing and Yahoo Answers: Everyone Knows
Something,Adamic, Lada A., Zhang Jun, Bakshy Eytan,
and Ackerman Mark S. , WWW2008, (2008)

34. Clear and consistent signatures
of an “Answer Person”
1
10
100
0 1 2 4 8 16 32 64
• Light touch to numerous threads initiated by
someone else
• Most ties are outward to local isolates
• Many more ties to small fish than big fish
34

35. Roles Project
Identify social
roles in
threaded
discussions
Next steps:
quantify &
explore in more
depth
35
Answer Person, microsoft.public.windows.server.general
Discussion, rec.kites
Flame, alt.flame
Social Support, alt.support.divorce
PUBLISHED in HICSS, JCMC, JoSS, IEEE Internet
Communications (special issue on Social Networks)

37. NodeXL: Network Overview,
Discovery and Exploration for Excel
Leverage spreadsheet for storage of
edge
and vertex data
http://www.codeplex.com/nodexl

38. The NodeXL Project Team

39. The NodeXL project is
Available via the
CodePlex Open Source
Project Hosting Site:
http://www.codeplex.com/nodexl

40. A minimal network can illustrate
the ways different locations have
different values for centrality and
degree
NodeXL
Network Overview Discovery and Exploration add-in for Excel 2007

41. Display community members sorted
by network attributes using Excel
Data|Sort

44. Resources to support
Use of NodeXL
Free
Tutorial/Manual
Data Sets
Available

45. NodeXL Tutorial
http://casci.umd.edu/

46. NodeXL: Display nodes with subgraph
images sorted by network attributes
using Excel Data|Sort

47. NodeXL: Filtered clusters

48. NodeXL: Import social networks
from email

49. NodeXL: Import social networks
from email

50. From Page Rank to People Rank
• People Rank is critical component of an effective community strategy.
• Communities are composed of a relatively small set of roles.
• Technology to identify these roles is critical for selecting high quality
content in a vast and diverse sea of material.
• Social Accounting Metadata is the raw material of social sorting, a people
rank that brings high quality content to the surface of an online
community.
• Reputations and profile are central to the effective management of a
community.

51. nTag: Electronic name badge

52. 52

53. SlamXR: Sensors, Routes, Community
SpotMe: Wireless device for meetings and events
Community Aspects: A Sociological Revolution?

54. 54

55. Trace Encounters: http://www.traceencounters.org/

56. Jabberwocky: Familiar stranger awareness
Community Aspects: A Sociological Revolution?

57. 57
Scott Counts, Marc
Smith, AJ Brush,
Paul Johns, Aaron Hoff

58. 58

59. Slam: Group-based communication
Slam
location map
Privacy
settings
Slam
UI
Scott Counts, Jordan Schwartz, Shelly Farnham
59

60. SlamXR: Sensors, Routes, Community
X 2,000,000,000 + = Lots of routes

61. Continuous data collection devices
Microsoft Research,
Cambridge, UK:
“SenseCam”

62. SLAM XR
62
Scott Counts, Marc Smith, Jianfeng Zhang,
Nuria Oliver, Andy Jacobs

63. 63

64. 64

66. 66

67. WIFE/MOTHER/WORKER/SPY
Does This Pencil Skirt Have an App?
http://www.nytimes.com/2009/09/24/fashion/24spy.html
“…a new iPhone app called Lose It! Which sounds like a
diet, if you ask me. For weeks he’d been keeping a food
diary on his phone — all the calories he ate, and all the
calories he burned — and it was constantly generating cool
little charts and graphs to let him know whether he was
meeting his goals.
“I’ve lost 12 pounds,” he said.
“Get it for me,” I hissed. “Now.”
Lose It! has its own database listing the calories in a few
thousand different foods. And if a food was not listed? I
could always find it in another iPhone app, the LiveStrong
calorie counter, which lists 450,000 foods.
LoseIt! Weight Loss iPhone App

68. Quantified Self:
people self-administer medical monitoring
Additional
sensors will
collect medical
data to improve
our health and
safety, as early
adopters in the
"Quantified Self"
movement make
clear.

69. CureTogether: http://www.curetogether.com/
Cure Together
People aggregate their self-generated medical data!

71. Risky behavior will be priced in
real time, 3rd glass of wine
tonight? Click here for a $20
extension for alcohol related
injury or illness.
http://www.connectedaction.net/2009/02
/18/the-future-of-helath-insurance-
mobile-medical-sensors-and-dynamic-
pricing/

72. http://www.ft.com/cms/s/0/c1473442-a6f4-11de-bd14-00144feabdc0.html
Novartis chip to help ensure bitter pills are
swallowed
By Andrew Jack in London
Published: September 21 2009 23:06 | Last
updated: September 21 2009 23:06
technology that inserts a tiny microchip into
each pill swallowed and sends a reminder to
patients by text message if they fail to follow
their doctors’ prescriptions.
the system – which broadcasts from the “chip
in the pill” to a receiver on the shoulder – on
20 patients using Diovan, a drug to lower
blood pressure, had boosted “compliance”
with prescriptions from 30 per cent to 80 per
cent after six months.

73. Prediction: a mobile App will be more
medically effective than many drugs
If only because it will make you take the drug
properly

74. ACLU Pizza
http://www.aclu.org/pizza/

75. Intel Health Guide
http://www.intel.com/pressroom/archive/releases/20080710corp_b.htm

76. Google Flu Tracker

77. SenseNetworks
Integrate a sensor grid to create
real time maps
of major cities,
create "tribes"
based on shared behavior.
http://www.sensenetworks.com/

78. Result: lives that are more publicly
displayed than ever before.
• Add potential improvements in audio and
facial recognition and a new world of
continuous observation and publication
emerges.
• Some benefits, like those displayed by the
Google Flu tracking system, illustrate the
potential for insight from aggregated sensor
data.
• More exploitative applications are also likely.

79. Information wants to be copied

80. Bits exist along a gradient
from private to public.
• But in practice they only move in one
direction.

81. Strong links
between
people and
content…

82. …are as strong
as the weakest link

83. Patterns of connection
may uniquely identify
De-anonymizing Social Networks
Arvind Narayanan & Vitaly Shmatikov
http://33bits.org/2009/03/19/de-anonymizing-social-networks/
Abstract:
Operators of online social networks are increasingly sharing potentially sensitive information
about users and their relationships with advertisers, application developers, and data-mining
researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks and develop a
new re-identification algorithm targeting anonymized social-network graphs. To demonstrate its
effectiveness on real-world networks, we show that a third of the users who can be verified to
have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-
sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate. Our
de-anonymization algorithm is based purely on the network topology, does not require creation
of a large number of dummy “sybil” nodes, is robust to noise and all existing defenses, and works
even when the overlap between the target network and the adversary’s auxiliary information is
small.

84. Cryptography weakens over time
Eventually, private bits,
even when encrypted,
become public because
the march of computing
power makes their
encryption increasingly
trivial to break.

85. No one expects
privacy to be
perfect in the
physical world.

86. Unintended cascades
• Taking a photo or updating a status message
can now set off a series of unpredictable
events.

87. Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
http://www.codeplex.com/nodexl
http://www.twitter.com/marc_smith
http://delicious.com/marc_smith/Paper
http://www.flickr.com/photos/marc_smith
http://www.facebook.com/marc.smith.sociologist
http://www.linkedin.com/in/marcasmith
http://www.slideshare.net/Marc_A_Smith
Mobile social media networks