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

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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.

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.

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  • Full Name Full Name Comment goes here.
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  • Very good analysis.

    Mark Chang, www.free-ringtones.co.in/ www.free-ringtones-for-sprint.com/
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  • “You can make a mess.”
  • A tutorial on analyzing social media networks is available from: casci.umd.edu/NodeXL_TeachingDifferent positions within a network can be measured using network metrics.
  • Research projects at Microsoft demonstrate the emergence of continuous data collection tools. These were applied to assist Alzheimer’s patients improve their recall of prior events.
  • Track your tracks with Path Tracks – monitor biking, skating, running performance
  • Applications are already getting “persuasive” – encouraging positive behaviors by tracking improvement or compliance.
  • Not only are people recording intimate medical data about themselves on an on-going basis, they are *publishing* this data to shared communities on the web. The goal is to aggregate data and insights into self treatment to build evidence and guidance for improved treatment.
  • Better tools for remote monitoring of chronic medical care patients.
  • The aggregate data from social media is creating new opportunities for gaining insights into macro trends across populations.
  • New sources of data and sensors attached to mobile devices are making new levels of awareness of real time social activity possible.
  • http://www.flickr.com/photos/53366513@N00/67046506/sizes/o/
  • http://www.flickr.com/photos/lizjones/1571656758/sizes/o/
  • http://www.flickr.com/photos/kjander/3123883124/sizes/o/
  • http://www.flickr.com/photos/shinythings/154815871/sizes/l/
  • http://www.flickr.com/photos/aussiegall/297237720/sizes/o/

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

  • 1. 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
  • 2. Youse.
    Y’all.
    Yes, youse.
    2
  • 3. A place apart
    Mobile Social Software
    “MoSoSo”
    A part of every place
  • 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. 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)
  • 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
    Motivations for contribution to public goods
    Source: xkcd, http://xkcd.com/386/
  • 12. 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”
  • 13. 13
  • 14. 14
  • 15. 15
  • 16. 16
  • 17. 17
  • 18. 18
  • 19. 19
    AnswerPersonSignatures
    Discussion
    People
  • 20. Discussion Starter
    Spammer
    Reply orientedDiscussion
    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 NetworkTheory
  • 23. SNA 101
    • Node
    • 24. “actor” on which relationships act; 1-mode versus 2-mode networks
    • 25. Edge
    • 26. Relationship connecting nodes; can be directional
    • 27. Cohesive Sub-Group
    • 28. Well-connected group; clique; cluster
    • 29. Key Metrics
    • 30. Centrality (group or individual measure)
    • 31. Number of direct connections that individuals have with others in the group (usually look at incoming connections only)
    • 32. Measure at the individual node or group level
    • 33. Cohesion (group measure)
    • 34. Ease with which a network can connect
    • 35. Aggregate measure of shortest path between each node pair at network level reflects average distance
    • 36. Density (group measure)
    • 37. Robustness of the network
    • 38. Number of connections that exist in the group out of 100% possible
    • 39. Betweenness (individual measure)
    • 40. # shortest paths between each node pair that a node is on
    • 41. Measure at the individual node level
    • 42. Node roles
    • 43. Peripheral – below average centrality
    • 44. Central connector – above average centrality
    • 45. Broker – above average betweenness
    A
    B
    C
    A
    B
    D
    E
    D
    E
    G
    F
    C
    D
    H
    I
    E
  • 46. SNA Resources
  • 47. The Ties that Blind?
    25
  • 48. Reply-To Network
    Network at distance 2 for the most prolific author of the microsoft.public.internetexplorer.general newsgroup
    The Ties that Blind?
  • 49. Darwin Bell
    27
  • 50. The Ties that Blind?
    Pajek without modification can sometimes reveal structures of great interest.
  • 51. Mapping Newsgroup Social Ties
    Microsoft.public.windowsxp.server.general
    Two “answer people” with an emerging 3rd.
    29
  • 52. 30
  • 53. 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
  • 54. 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
  • 55. 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)
  • 56. 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
  • 57. 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)
  • 58.
  • 59. NodeXL: Network Overview, Discovery and Exploration for Excel
    Leverage spreadsheet for storage of edge and vertex data
    http://www.codeplex.com/nodexl
  • 60. The NodeXL Project Team
  • 61. The NodeXL project is Available via the CodePlex Open Source Project Hosting Site:http://www.codeplex.com/nodexl
  • 62. 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
  • 63. Display community members sorted by network attributes using Excel Data|Sort
  • 64.
  • 65.
  • 66. Resources to supportUse of NodeXLFree Tutorial/ManualData SetsAvailable
  • 67. NodeXL Tutorial
    http://casci.umd.edu/
  • 68. NodeXL: Display nodes with subgraphimages sorted by network attributes using Excel Data|Sort
  • 69. NodeXL: Filtered clusters
  • 70. NodeXL: Import social networks from email
  • 71. NodeXL: Import social networks from email
  • 72. 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.
     
  • 73. nTag: Electronic name badge
  • 74. 52
  • 75. SlamXR: Sensors, Routes, Community
    Community Aspects: A Sociological Revolution?
    SpotMe: Wireless device for meetings and events
  • 76. 54
  • 77. Trace Encounters: http://www.traceencounters.org/
  • 78. Community Aspects: A Sociological Revolution?
    Jabberwocky: Familiar stranger awareness
  • 79. 57
    Scott Counts, Marc Smith, AJ Brush,
    Paul Johns, Aaron Hoff
  • 80. 58
  • 81. Slam: Group-based communication
    Slam location map
    Slam UI
    Privacy settings
    Scott Counts, Jordan Schwartz, Shelly Farnham
    59
  • 82. SlamXR: Sensors, Routes, Community
    =
    +
    Lots of routes
    X 2,000,000,000
  • 83. Continuous data collection devices
    Microsoft Research, Cambridge, UK: “SenseCam”
  • 84. SLAM XR
    62
    Scott Counts, Marc Smith, Jianfeng Zhang, Nuria Oliver, Andy Jacobs
  • 85. 63
  • 86. 64
  • 87.
  • 88. 66
  • 89. 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
  • 90. 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.
  • 91. CureTogether: http://www.curetogether.com/
    Cure Together
    People aggregate their self-generated medical data!
  • 92.
  • 93. 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/
  • 94. 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.
  • 95. Prediction: a mobile App will be more medically effective than many drugs
    If only because it will make you take the drug properly
  • 96. ACLU Pizza
    http://www.aclu.org/pizza/
  • 97. Intel Health Guide
    http://www.intel.com/pressroom/archive/releases/20080710corp_b.htm
  • 98. Google Flu Tracker
  • 99. SenseNetworks
    Integrate a sensor grid to create real time maps of major cities, create "tribes" based on shared behavior.
    http://www.sensenetworks.com/
  • 100. 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.
  • 101. Information wants to be copied
  • 102. Bits exist along a gradient from private to public.
    But in practice they only move in one direction.
  • 103. Strong links between people and content…
  • 104. …are as strong as the weakest link
  • 105. 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.
  • 106. 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.
  • 107. No one expects privacy to be perfect in the physical world.
  • 108. Unintended cascades
    Taking a photo or updating a status message can now set off a series of unpredictable events.
  • 109. 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