Post on 28-Jan-2015
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Marc A. SmithChief Social ScientistConnected Action Consulting Group
Marc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smith
Mobile social media networks
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Youse.Y’all.
Yes, youse.
A place apart
A part of every place
Mobile Social Software“MoSoSo”
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Email (and more) is from people to people
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Patterns are left behind
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.
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)
Innovations in the interaction order:
45,000 years ago: Speech, body adornment10,000 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.
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• 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.
Nobel in Economics
2009
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Source: xkcd, http://xkcd.com/386/
Motivations for contribution to public goods
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”
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AnswerPerson
Signatures
DiscussionPeople
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Spammer
Discussion Starter
Reply orientedDiscussion
FlameWarrior
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• 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
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SNA Resources
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The Ties that Blind?
Reply-To NetworkNetwork at distance 2 for the most prolific author of the microsoft.public.internetexplorer.general newsgroup
The Ties that Blind?
27Darwin Bell
Pajek without modification can sometimes reveal structures of great interest.
The Ties that Blind?
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Mapping Newsgroup Social Ties
Microsoft.public.windowsxp.server.general
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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
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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
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)
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Clear and consistent signaturesof an “Answer Person”
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• 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
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Roles ProjectIdentify social
roles in threaded
discussionsNext steps: quantify &
explore in more depth
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
http://www.codeplex.com/nodexl
The NodeXL Project Team
The NodeXL project is Available via the CodePlex Open Source Project Hosting Site:http://www.codeplex.com/nodexl
A minimal network can illustrate the ways different locations have different values for centrality and
degreeDi
ane h
as h
igh
degr
ee
Heather has high
betweeness
NodeXLNetwork Overview Discovery and Exploration add-in for Excel 2007
Display community members sorted by network attributes using Excel Data|Sort
Resources to supportUse of NodeXL
Free Tutorial/Manual
Data SetsAvailable
NodeXL Tutorial
http://casci.umd.edu/
NodeXL: Display nodes with subgraph images sorted by network attributes using Excel Data|Sort
NodeXL: Filtered clusters
NodeXL: Import social networks from email
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
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SlamXR: Sensors, Routes, Community
SpotMe: Wireless device for meetings and events
Community Aspects: A Sociological Revolution?
Jabberwocky: Familiar stranger awareness
Community Aspects: A Sociological Revolution?
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Scott Counts, Marc Smith, AJ Brush,
Paul Johns, Aaron Hoff
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Slam: Group-based communication
Slam location map
Privacy settings
Slam UI
Scott Counts, Jordan Schwartz, Shelly Farnham
SlamXR: Sensors, Routes, Community
X 2,000,000,000 + = Lots of routes
Continuous data collection devices
Microsoft Research, Cambridge, UK: “SenseCam”
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SLAM XRScott Counts, Marc Smith, Jianfeng Zhang,
Nuria Oliver, Andy Jacobs
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WIFE/MOTHER/WORKER/SPYDoes 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 TogetherPeople 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 swallowedBy Andrew Jack in LondonPublished: 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
Intel Health Guidehttp://www.intel.com/pressroom/archive/releases/20080710corp_b.htm
Google Flu Tracker
SenseNetworksIntegrate 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.
Patterns of connection may uniquely identify
De-anonymizing Social Networks Arvind Narayanan & Vitaly Shmatikovhttp://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.
Cryptography weakens over timeEventually, 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.
Marc A. SmithChief Social ScientistConnected Action Consulting Group
Marc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smith
Mobile social media networks