2009 - Connected Action - Marc Smith - Social Media Network Analysis

Mobile social media networks
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

Youse.
Y’all.
Yes, youse.
2

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

4
Email (and more) is from people to people

Patterns are left behind
5

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.

InteractionistSociology
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)

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

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

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

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

19
AnswerPersonSignatures
Discussion
People

Discussion Starter
Spammer
Reply orientedDiscussion
Flame
Warrior
20

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 NetworkTheory

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
A
B
C
A
B
D
E
D
E
G
F
C
D
H
I
E

SNA Resources

The Ties that Blind?
25

Reply-To Network
Network at distance 2 for the most prolific author of the microsoft.public.internetexplorer.general newsgroup

Darwin Bell
27

Pajek without modification can sometimes reveal structures of great interest.

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

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

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

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

Clear and consistent signaturesof an “Answer Person”
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

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)

NodeXL: Network Overview, Discovery and Exploration for Excel
Leverage spreadsheet for storage of edge and vertex data

The NodeXL Project Team

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

NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007
Heather has high betweeness
A minimal network can illustrate the ways different locations have different values for centrality and degree
Diane has high degree

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

Resources to supportUse of NodeXLFree Tutorial/ManualData SetsAvailable

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

NodeXL: Display nodes with subgraphimages sorted by network attributes using Excel Data|Sort

NodeXL: Filtered clusters

NodeXL: Import social networks from email

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.

nTag: Electronic name badge

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

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

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

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

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

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

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

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

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

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.

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

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/

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.

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

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

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

Google Flu Tracker

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

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.

Information wants to be copied

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

Strong links between people and content…

…are as strong as the weakest link

Patterns of connection may uniquely identify
De-anonymizing Social Networks Arvind Narayanan & VitalyShmatikov
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 microbloggingservice, 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.

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.

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

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

Mobile social media networks
Marc A. Smith
Chief Social Scientist
Connected Action Consulting Group
Marc@connectedaction.net
http://www.connectedaction.net
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

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2009 - Connected Action - Marc Smith - Social Media Network Analysis

by Marc Smith on Oct 22, 2009

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