Mobile social media networksMarc A. SmithChief Social ScientistConnected Action Consulting GroupMarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paperhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smith

Youse.Y’all.Yes, youse.2

A place apartMobile Social Software“MoSoSo”A part of every place

4Email (and more) is from people to people

Patterns are left behind5

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

InteractionistSociologyCentral tenetFocus on the active effort of accomplishing interactionPhenomena of interestPresentation of self Claims to membershipJuggling multiple (conflicting) rolesFrontstage/Backstage Strategic interactionManaging one’s own and others’ “face”MethodsEthnography 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.

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. 10Nobel in Economics 2009

11Motivations for contribution to public goodsSource: 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”

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19AnswerPersonSignaturesDiscussionPeople

Discussion StarterSpammerReply orientedDiscussionFlameWarrior20

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Central tenet Social structure emerges from the aggregate of relationships (ties) among members of a populationPhenomena of interestEmergence of cliques and clusters from patterns of relationshipsCentrality (core), periphery (isolates), betweennessMethodsSurveys, 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.7-16Social NetworkTheory

SNA 101Node“actor” on which relationships act; 1-mode versus 2-mode networksEdgeRelationship connecting nodes; can be directionalCohesive Sub-GroupWell-connected group; clique; clusterKey MetricsCentrality (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 levelCohesion (group measure)Ease with which a network can connectAggregate measure of shortest path between each node pair at network level reflects average distanceDensity (group measure)Robustness of the networkNumber of connections that exist in the group out of 100% possible Betweenness (individual measure)# shortest paths between each node pair that a node is onMeasure at the individual node levelNode rolesPeripheral – below average centralityCentral connector – above average centralityBroker – above average betweennessABCABDEDEGFCDHIE

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

SNA Resources

The Ties that Blind?25

Reply-To NetworkNetwork at distance 2 for the most prolific author of the microsoft.public.internetexplorer.general newsgroupThe Ties that Blind?

Darwin Bell27

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

Mapping Newsgroup Social TiesMicrosoft.public.windowsxp.server.generalTwo “answer people” with an emerging 3rd.29

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Distinguishing attributes of online social rolesAnswer personOutward ties to local isolatesRelative absence of trianglesFew intense tiesReply MagnetTies from local isolates often inward onlySparse, few trianglesFew intense ties31

Distinguishing attributes:Answer personOutward ties to local isolatesRelative absence of trianglesFew intense tiesDiscussion personTies from local isolates often inward onlyDense, many trianglesNumerous intense ties32

Leading research: Adamic et al. 2008Knowledge 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 elseMost ties are outward to local isolatesMany more ties to small fish than big fish34

Roles ProjectIdentify social roles in threaded discussionsNext steps: quantify & explore in more depth35Answer Person, microsoft.public.windows.server.generalDiscussion, rec.kitesFlame, alt.flameSocial Support, alt.support.divorcePUBLISHED in HICSS, JCMC, JoSS, IEEE Internet Communications (special issue on Social Networks)

NodeXL: Network Overview, Discovery and Exploration for ExcelLeverage spreadsheet for storage of edge and vertex datahttp://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

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

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

Resources to supportUse of NodeXLFree Tutorial/ManualData SetsAvailable

NodeXL Tutorialhttp://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

NodeXL: Import social networks from email

From Page Rank to People RankPeople 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, CommunityCommunity Aspects: A Sociological Revolution?SpotMe: Wireless device for meetings and events

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

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

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

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Slam: Group-based communicationSlam location mapSlam UIPrivacy settingsScott Counts, Jordan Schwartz, Shelly Farnham59

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

Continuous data collection devicesMicrosoft Research, Cambridge, UK: “SenseCam”

SLAM XR62Scott 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 monitoringAdditional 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.htmlNovartis chip to help ensure bitter pills are swallowedBy Andrew Jack in LondonPublished: September 21 2009 23:06 | Last updated: September 21 2009 23:06technology 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 drugsIf only because it will make you take the drug properly

ACLU Pizzahttp://www.aclu.org/pizza/

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.

Strong links between people and content…

…are as strong as the weakest link

Patterns of connection may uniquely identifyDe-anonymizing Social Networks Arvind Narayanan & VitalyShmatikovhttp://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 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 cascadesTaking a photo or updating a status message can now set off a series of unpredictable events.

Mobile social media networksMarc A. SmithChief Social ScientistConnected Action Consulting GroupMarc@connectedaction.nethttp://www.connectedaction.nethttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://delicious.com/marc_smith/Paperhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologisthttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smith