Presentation to NIA

1. A project from the Social Media Research Foundation: http://www.smrfoundation.org
Smart
Society
and
Civic
Culture

2. About Me
Introductions
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
http://www.smrfoundation.org

3. Citizens are
listening and participating in
social media
• Leveraging social media for sustaining civil society
• Finding government services
• Citizen interactions
• Measuring public opinion
• Identifying influential opinions
• Summarizing topics of interest
• Evaluating your efforts to engage in social media

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

5. Patterns are left behind

6. http://www.flickr.com/photos/amycgx/3119640267/

8. Social Media Systems

9. World Wide Web
Each contains one or more
social networks

10. Early Steps
http://iparticipate.wikispaces.com
Informal Gathering
College Park, MD, April 2009
Article: Science March 2009
BEN SHNEIDERMAN

11. www.tmsp.umd.edu
NSF Workshops: Palo Alto & DC

12. International Efforts
intlsocialparticipation.net
Community Informatics
Research Network

13. Common goods that require controlled consumption
http://flickr.com/photos/himalayan-trails/275941886/

14. Collective Action Dilemma Theory
• Central tenet
– Individual rationality leads to collective disaster
• Phenomena of interest
– Provision and/or sustainable consumption of collective
resources
– Public Goods, Common Property, "Free Rider” Problems,
Tragedies
– Signaling intent
• Methods
– Surveys, interviews, participant observation, log file
analysis, computer modeling
(Axelrod, 1984; Hess, 1995; Kollock & Smith, 1996)
Community Computer Mediated Collective Action

15. Common goods that require collective contribution
http://flickr.com/photos/jose1jose2jose3/241450368/

16. World Wide Web
Each contains one or more
social networks

17. Telecom networks are social networks

18. 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

19. 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.
19

20. Location, Location, Location

21. Network of connections among “UMich” mentioning Twitter users
Position, Position, Position

22. There are many kinds of ties….
http://www.flickr.com/photos/stevendepolo/3254238329

23. “Think Link”
Nodes & Edges
Is related to
A B
In and Out Degree

24. “Think Link”
Nodes & Edges
Is related to
A B
Ties of different types
Edits
Shares membership

25. “Think Link”
Nodes & Edges
Is related to
Person Document
Nodes of different types
Edits
Shares membership

26. Collections of Connections
Centralities
• Degree
• Closeness
• Betweenness
• Eigenvector
http://en.wikipedia.org/wiki/Centrality

27. 27

28. Social
Networks
• History:
from the
dawn of
time!
• Theory and
method:
1934 ->
• Jacob L.
Moreno
• http://en.wik
ipedia.org/wi
ki/Jacob_L._
Moreno

29. • 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
http://en.wikipedia.org/wiki/Social_network

30. NodeXL
Network Overview Discovery and Exploration add-in for Excel 2007/2010
A minimal network can
illustrate the ways different
locations have different values
for centrality and degree

32. yes no
I like you I really like youI kind of like you
I feel socially obligated to link to youI know you
I wish I knew you I like your picture You are cool
I was paid to link to you I want your reflected glory
Everybody else links to you I’d vote for you
We met at a conference and it seemed like the thing to do.
Can I date you?
I beat you on Xbox Live Hi, Mom I have fake alter egos

33. yes no

34. SOCIAL NETWORKS
IN TELECOM NETWORKS
Social media platforms
are a source of multiple
Social network data sets:
“Calls”
“Friends”
“Replies”
“Follows”
“Comments”
“Reads”
“Co-edits”
“Co-mentions”
“Co-locates”
“Hybrids”

35. New Tie Granularities
• Named as friends
• Reply to message
• Poke, wave, view image
• “Gift”, “Scrap”, “Ice Cubes”
• Was in the same place
• Laptop is nearby
• Edited same web page

36. Two “answer people” with an emerging 3rd.
Mapping
Newsgroup
Social
Ties
Microsoft.public.windowsxp.server.general
36

37. 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)

38. Communities
in Cyberspace

39. Analyzing Social Media Networks with NodeXL
I. Getting Started with Analyzing Social Media
Networks
1. Introduction to Social Media and Social Networks
2. Social media: New Technologies of Collaboration
3. Social Network Analysis
II. NodeXL Tutorial: Learning by Doing
4. Layout, Visual Design & Labeling
5. Calculating & Visualizing Network Metrics
6. Preparing Data & Filtering
7. Clustering &Grouping
III Social Media Network Analysis Case Studies
8. Email
9. Threaded Networks
10. Twitter
11. Facebook
12. WWW
13. Flickr
14. YouTube
15. Wiki Networks
www.elsevier.com/wps/find/bookdescription.cws_home/723354/description

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

43. Import from multiple
social media network
sources

48. Welser, Howard T., Eric Gleave, Danyel Fisher,
and Marc Smith. 2007. Visualizing the Signatures
of Social Roles in Online Discussion Groups.
The Journal of Social Structure. 8(2).
Experts and “Answer People”
Discussion starters, Topic setters
Discussion people, Topic setters

51. E-mail Communication – Organization Units
• Email from the TechABC’s
organizational unit
network “backbone”,
focusing on high-traffic
connections between
units > 50 messages per
FTE.
• Color is mapped to
Betweenness Centrality
• Green vertices play
important roles as bridge
spanners.
• Excluded nodes with low
Closeness Centrality to
filter out vertices that are
not part of the large

52. Graph Motifs

53. http://www.youtube.com/watch?v=0M3T65Iw3Ac
NodeXL Video

54. NodeXL
Free/Open Social Network Analysis add-in for Excel 2007 makes graph theory
as easy as a bar chart, integrated analysis of social media sources.
http://nodexl.codeplex.com

55. Bernie Hogan is a Research Fellow at the Oxford Internet
Institute at the University of Oxford. Bernie's work focuses
on the process of networking, or maintaining connections
with other people. His dissertation focused on the use of
multiple media for networking while his current research on
Facebook looks at the complexities of networking with
multiple groups on a single site.

56. Facebook “ego” networks

57. Network Visualization
by Semantic
Substrates
Ben Shneiderman, Senior Member,
IEEE,
and Aleks Aris
IEEE TRANSACTIONS ON VISUALIZATION
AND COMPUTER GRAPHICS, VOL. 12, NO. 5,
SEPTEMBER/OCTOBER 2006
A starting list for high priority tasks on basic networks includes:
T1) count number of nodes and links
T2) for every node, count degree
T3) for every node, find the nodes that are distance 1, 2, 3
…away
T4) for every node, find betweenness centrality
T5) for every node, find structural prestige
T6) find diameter of the network
T7) identify strongly connected or compact clusters
T8) for a given pair of nodes, find shortest path between them
When moving up to C2 and C3, where labels are allowed, additional tasks might be:
T9) for every node/link, read the label
T10) find all nodes/links with a given label/attribute

58. Explicit vs. implicit “reputation systems”
Explicit
Statements about behaviors and
relationships
• eBay
• Amazon
• Slashdot
• Digg
• MySpace
• Facebook
• YouTube
• flickr
Issues:
• Provisioning: not enough rating
• Latency: ratings not fast enough
• Bias: susceptible to initial reactions
• Collusion: easily “shilled”
• Inflation: disincentives to accuracy
Implicit
Observations about behaviors and
relationships
• Google
• Amazon
• Flickr, MySpace, Facebook
• del.icio.us
• Technorati
• Netscan
Issues:
• Ambiguity: Behavior is not
endorsement
• Collusion: Subject to manipulation
• May be subject to “herding” or
positive-feedback loops

59. 59
Source: xkcd, http://xkcd.com/386/
Motivations for
contribution to
public goods

60. Summary: SNA tells you:
• Macro:
– What is the “shape” of the crowd?
– Are there sub-groups/clusters?
• Micro:
– Who is at the “center”?
– Who is at the “edge”?
– Who is the “bridge”?

61. NodeXL – next steps
• Time is of the essence!
– Contrast graph A and B
– Time series analysis of many “frames”
• Keyword networks
– “semantic” associations in social media
• “The Web”
– Browser-based interface to NodeXL
• Federated Data Collection
– Array of many collectors sharing resulting data

62. Join the
Social Media Research Foundation
• Contribute to the NodeXL project
– Developers, users, researchers are welcome!
• Join the distributed data collection project
– Run the data collector and share your results
• Apply our tools and data to your research!
http://www.smrfoundation.org

63. What makes it social?
• Who makes it?
• Who consumes it?
• Who owns it?/Who profits from it?
• Who or what makes it successful?
• How to harness the swarm?
• How to map and understand its dynamics?
– How do people and group vary?
– Who links to whom?
• What is next for social media?

64. Dyadic exchanges.
Email to named
individual(s)
Committee reports to
a decision
maker/reviewer
Professional services
reports for decision makers
Local email list
“Social” blogs
Personal social
network profile page
Multiple authored
specialty publications
Group blogs.
Personal social
networks
Professional reports to
specialty groups
Value added economic data
Bloomberg
Messages to
discussion
groups/web board
Sole authored
source code
Popular blogs
Novels
Multiple authored
popular media,
software
Journalism
Wikipedia Pages
Popular group blogs
Collective search engine
users
Market behavior
Query log optimizations
Market analysis
How large are the social groups
producing and consuming social media?
Individuals
Small Groups
Large Groups
Individuals
Small Groups
Large Groups
Producers
Consumers

65. Digital
Object
Editing
Granularity
Fine
(Character/Pixel/Byte)
Medium
(Object/Attribute/Track/Player)
Coarse
(Document/Message/Blog
Post/Photo)
Digital
Object
Editing
Synchronicity
Each user can directly
control smallest units
of content.
Each user controls medium
sized blocks of content that
can only indirectly alter or be
altered by other user’s content
in a larger shared data
structure.
Each user controls a block
of content, rarely edited or
modified by others with
only associative linkages.
Synchronous Real time Shared
canvas
Virtual Worlds
Multiplayer Games
Real-time networked musical
jamming
Chat, IM, Twitter
Asynchronous Shared docs, images,
video, audio
Source code
Wikipedia
Contribution to collected works
(album, anthology, report
section, discussion group,
photosets and other
collections).
Email
Blog posts
Link sharing
Photo sharing
Document sharing
Turn based games
Dimensions of Social Media:
How large are the pieces of social media?
How interactive is the rate of exchange?

66. Dimensions of Social Media:
Who can exercise what property rights
over social media?
Author
Group of
authors
Recipients Observers Host
Public
Domain
Types of
property rights
“What does it mean to own
social media content?”
Create?
Copy/Paste?
Edit/Delete?
Limit access?
Revoke access?
Monitor access?
Transfer to new host?
Transfer rights to others?
Commercial exploitation?
Adjoining display rights?
(can I put ads near your content when
I show it to other people)?
Aggregation and secondary
analysis rights?
Who owns social media content?

67. 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.

68. 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)

69. 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

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

71. "All phones will be smartphones
eventually," Sanjay Jha, chief
executive of Motorola's mobile
phone business said during a recent
interview with the Financial Times.
Smartphone sales in the US will climb
steadily over the next 18 months and
account for just under 50 per cent of
total sales by the autumn of next
year
http://www.ft.com/cms/s/0/1d7e83b8-3b93-11df-a4c0-00144feabdc0.html

72. Auto-Tweet Your Weight to the World
By ERIC A. TAUB
If you’re losing weight, why keep it to yourself? Now the whole world can
know, automatically.
With the Wi-Fi Connected Body Scale from Withings, a French company,
everyone can know your body weight, lean and fat mass, and your B.M.I., or
body mass index.
The $159 scale, available from the company’s Web site and Amazon.com,
automatically keeps track of up to eight users’ body stats. Step on the scale,
and electrical impedance figures out your body fat. It then sends all the
information via Wi-Fi to a no-charge Web site, a free iPhone app, Twitter,
Google Health and Microsoft HealthVault, among others.
The idea is that by amassing a continual flow of data, you’ll be able to
monitor your progress in maintaining, or achieving, a healthy life style.
The Wi-Fi Connected Body Scale keeps track of your weight in pounds,
kilograms or stones (used in Great Britain., one stone equals 14 pounds). The
eight users are distinguished by their weight.
If you don’t want everyone to see the ups and downs of your avoirdupois, you
can always create a Twitter account that only you (and your doctor) know
about.

73. 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
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.

74. 74

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

76. Poken makes social exchanges simple and cheap:

77. FitBit consumer activity monitoring.

79. http://www.intel.com/healthcare/telehealth/

80. 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

81. 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.

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

84. 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/

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

86. A project from the Social Media Research Foundation: http://www.smrfoundation.org
Smart
Society
and
Civic
Culture