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.

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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

    1. 1. 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 Mobile social media networks
    2. 2. Youse.Y’all. Yes, youse. 2
    3. 3. A place apart A part of every place Mobile Social Software “MoSoSo”
    4. 4. 4 Email (and more) is from people to people
    5. 5. Patterns are left behind 5
    6. 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. 7. 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)
    8. 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. 9. Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.
    10. 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. 11 Source: xkcd, http://xkcd.com/386/ Motivations for contribution to public goods
    12. 12. Social media usage generates Social Networks Social media platforms are a source of multiple Social network data sets: “Friends” “Replies” “Follows” “Comments” “Reads” “Co-edits” “Co-mentions” “Hybrids”
    13. 13. 13
    14. 14. 14
    15. 15. 15
    16. 16. 16
    17. 17. 17
    18. 18. 18
    19. 19. 19 Answer Person Signatures Discussion People
    20. 20. Spammer Discussion Starter Reply oriented Discussion Flame Warrior 20
    21. 21. 21
    22. 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 Network Theory
    23. 23. 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
    24. 24. SNA Resources
    25. 25. The Ties that Blind? 25
    26. 26. Reply-To Network Network at distance 2 for the most prolific author of the microsoft.public.internetexplorer.general newsgroup The Ties that Blind?
    27. 27. Darwin Bell 27
    28. 28. Pajek without modification can sometimes reveal structures of great interest. The Ties that Blind?
    29. 29. Two “answer people” with an emerging 3rd. Mapping Newsgroup Social Ties Microsoft.public.windowsxp.server.general 29
    30. 30. 30
    31. 31. 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
    32. 32. 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
    33. 33. 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)
    34. 34. Clear and consistent signatures of an “Answer Person” 1 10 100 0 1 2 4 8 16 32 64 • 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
    35. 35. 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)
    36. 36. NodeXL: Network Overview, Discovery and Exploration for Excel Leverage spreadsheet for storage of edge and vertex data http://www.codeplex.com/nodexl
    37. 37. The NodeXL Project Team
    38. 38. The NodeXL project is Available via the CodePlex Open Source Project Hosting Site: http://www.codeplex.com/nodexl
    39. 39. A minimal network can illustrate the ways different locations have different values for centrality and degree NodeXL Network Overview Discovery and Exploration add-in for Excel 2007
    40. 40. Display community members sorted by network attributes using Excel Data|Sort
    41. 41. Resources to support Use of NodeXL Free Tutorial/Manual Data Sets Available
    42. 42. NodeXL Tutorial http://casci.umd.edu/
    43. 43. NodeXL: Display nodes with subgraph images sorted by network attributes using Excel Data|Sort
    44. 44. NodeXL: Filtered clusters
    45. 45. NodeXL: Import social networks from email
    46. 46. NodeXL: Import social networks from email
    47. 47. 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.
    48. 48. nTag: Electronic name badge
    49. 49. 52
    50. 50. SlamXR: Sensors, Routes, Community SpotMe: Wireless device for meetings and events Community Aspects: A Sociological Revolution?
    51. 51. 54
    52. 52. Trace Encounters: http://www.traceencounters.org/
    53. 53. Jabberwocky: Familiar stranger awareness Community Aspects: A Sociological Revolution?
    54. 54. 57 Scott Counts, Marc Smith, AJ Brush, Paul Johns, Aaron Hoff
    55. 55. 58
    56. 56. Slam: Group-based communication Slam location map Privacy settings Slam UI Scott Counts, Jordan Schwartz, Shelly Farnham 59
    57. 57. SlamXR: Sensors, Routes, Community X 2,000,000,000 + = Lots of routes
    58. 58. Continuous data collection devices Microsoft Research, Cambridge, UK: “SenseCam”
    59. 59. SLAM XR 62 Scott Counts, Marc Smith, Jianfeng Zhang, Nuria Oliver, Andy Jacobs
    60. 60. 63
    61. 61. 64
    62. 62. 66
    63. 63. 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
    64. 64. 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.
    65. 65. CureTogether: http://www.curetogether.com/ Cure Together People aggregate their self-generated medical data!
    66. 66. 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/
    67. 67. 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.
    68. 68. Prediction: a mobile App will be more medically effective than many drugs If only because it will make you take the drug properly
    69. 69. ACLU Pizza http://www.aclu.org/pizza/
    70. 70. Intel Health Guide http://www.intel.com/pressroom/archive/releases/20080710corp_b.htm
    71. 71. Google Flu Tracker
    72. 72. SenseNetworks Integrate a sensor grid to create real time maps of major cities, create "tribes" based on shared behavior. http://www.sensenetworks.com/
    73. 73. 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.
    74. 74. Information wants to be copied
    75. 75. Bits exist along a gradient from private to public. • But in practice they only move in one direction.
    76. 76. Strong links between people and content…
    77. 77. …are as strong as the weakest link
    78. 78. Patterns of connection may uniquely identify De-anonymizing Social Networks Arvind Narayanan & Vitaly Shmatikov 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 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.
    79. 79. 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.
    80. 80. No one expects privacy to be perfect in the physical world.
    81. 81. Unintended cascades • Taking a photo or updating a status message can now set off a series of unpredictable events.
    82. 82. 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 Mobile social media networks