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2010-November-8-NIA - Smart Society and Civic Culture - Marc Smith


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Presentation to NIA

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2010-November-8-NIA - Smart Society and Civic Culture - Marc Smith

  1. 1. A project from the Social Media Research Foundation: Smart Society and Civic Culture
  2. 2. About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group
  3. 3. Citizens are listening and participating in social media • Leveraging social media for sustaining civil society • Finding government services • Citizen interactions • Measuring public opinion • Identifying influential opinions • Summarizing topics of interest • Evaluating your efforts to engage in social media
  4. 4. 4 Email (and more) is from people to people
  5. 5. Patterns are left behind
  6. 6.
  7. 7. Social Media Systems
  8. 8. World Wide Web Each contains one or more social networks
  9. 9. Early Steps Informal Gathering College Park, MD, April 2009 Article: Science March 2009 BEN SHNEIDERMAN
  10. 10. NSF Workshops: Palo Alto & DC
  11. 11. International Efforts Community Informatics Research Network
  12. 12. Common goods that require controlled consumption
  13. 13. Collective Action Dilemma Theory • Central tenet – Individual rationality leads to collective disaster • Phenomena of interest – Provision and/or sustainable consumption of collective resources – Public Goods, Common Property, "Free Rider” Problems, Tragedies – Signaling intent • Methods – Surveys, interviews, participant observation, log file analysis, computer modeling (Axelrod, 1984; Hess, 1995; Kollock & Smith, 1996) Community Computer Mediated Collective Action
  14. 14. Common goods that require collective contribution
  15. 15. World Wide Web Each contains one or more social networks
  16. 16. Telecom networks are social networks
  17. 17. 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
  18. 18. 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. 19
  19. 19. Location, Location, Location
  20. 20. Network of connections among “UMich” mentioning Twitter users Position, Position, Position
  21. 21. There are many kinds of ties….
  22. 22. “Think Link” Nodes & Edges Is related to A B In and Out Degree
  23. 23. “Think Link” Nodes & Edges Is related to A B Ties of different types Edits Shares membership
  24. 24. “Think Link” Nodes & Edges Is related to Person Document Nodes of different types Edits Shares membership
  25. 25. Collections of Connections Centralities • Degree • Closeness • Betweenness • Eigenvector
  26. 26. 27
  27. 27. Social Networks • History: from the dawn of time! • Theory and method: 1934 -> • Jacob L. Moreno • http://en.wik ki/Jacob_L._ Moreno
  28. 28. • 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
  29. 29. NodeXL Network Overview Discovery and Exploration add-in for Excel 2007/2010 A minimal network can illustrate the ways different locations have different values for centrality and degree
  30. 30. yes no I like you I really like youI kind of like you I feel socially obligated to link to youI know you I wish I knew you I like your picture You are cool I was paid to link to you I want your reflected glory Everybody else links to you I’d vote for you We met at a conference and it seemed like the thing to do. Can I date you? I beat you on Xbox Live Hi, Mom I have fake alter egos
  31. 31. yes no
  32. 32. SOCIAL NETWORKS IN TELECOM NETWORKS Social media platforms are a source of multiple Social network data sets: “Calls” “Friends” “Replies” “Follows” “Comments” “Reads” “Co-edits” “Co-mentions” “Co-locates” “Hybrids”
  33. 33. New Tie Granularities • Named as friends • Reply to message • Poke, wave, view image • “Gift”, “Scrap”, “Ice Cubes” • Was in the same place • Laptop is nearby • Edited same web page
  34. 34. Two “answer people” with an emerging 3rd. Mapping Newsgroup Social Ties Microsoft.public.windowsxp.server.general 36
  35. 35. 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)
  36. 36. Communities in Cyberspace
  37. 37. Analyzing Social Media Networks with NodeXL I. Getting Started with Analyzing Social Media Networks 1. Introduction to Social Media and Social Networks 2. Social media: New Technologies of Collaboration 3. Social Network Analysis II. NodeXL Tutorial: Learning by Doing 4. Layout, Visual Design & Labeling 5. Calculating & Visualizing Network Metrics 6. Preparing Data & Filtering 7. Clustering &Grouping III Social Media Network Analysis Case Studies 8. Email 9. Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks
  38. 38. NodeXL: Network Overview, Discovery and Exploration for Excel Leverage spreadsheet for storage of edge and vertex data
  39. 39. Import from multiple social media network sources
  40. 40. Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). Experts and “Answer People” Discussion starters, Topic setters Discussion people, Topic setters
  41. 41. E-mail Communication – Organization Units • Email from the TechABC’s organizational unit network “backbone”, focusing on high-traffic connections between units > 50 messages per FTE. • Color is mapped to Betweenness Centrality • Green vertices play important roles as bridge spanners. • Excluded nodes with low Closeness Centrality to filter out vertices that are not part of the large
  42. 42. Graph Motifs
  43. 43. NodeXL Video
  44. 44. NodeXL Free/Open Social Network Analysis add-in for Excel 2007 makes graph theory as easy as a bar chart, integrated analysis of social media sources.
  45. 45. Bernie Hogan is a Research Fellow at the Oxford Internet Institute at the University of Oxford. Bernie's work focuses on the process of networking, or maintaining connections with other people. His dissertation focused on the use of multiple media for networking while his current research on Facebook looks at the complexities of networking with multiple groups on a single site.
  46. 46. Facebook “ego” networks
  47. 47. Network Visualization by Semantic Substrates Ben Shneiderman, Senior Member, IEEE, and Aleks Aris IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 12, NO. 5, SEPTEMBER/OCTOBER 2006 A starting list for high priority tasks on basic networks includes: T1) count number of nodes and links T2) for every node, count degree T3) for every node, find the nodes that are distance 1, 2, 3 …away T4) for every node, find betweenness centrality T5) for every node, find structural prestige T6) find diameter of the network T7) identify strongly connected or compact clusters T8) for a given pair of nodes, find shortest path between them When moving up to C2 and C3, where labels are allowed, additional tasks might be: T9) for every node/link, read the label T10) find all nodes/links with a given label/attribute
  48. 48. Explicit vs. implicit “reputation systems” Explicit Statements about behaviors and relationships • eBay • Amazon • Slashdot • Digg • MySpace • Facebook • YouTube • flickr Issues: • Provisioning: not enough rating • Latency: ratings not fast enough • Bias: susceptible to initial reactions • Collusion: easily “shilled” • Inflation: disincentives to accuracy Implicit Observations about behaviors and relationships • Google • Amazon • Flickr, MySpace, Facebook • • Technorati • Netscan Issues: • Ambiguity: Behavior is not endorsement • Collusion: Subject to manipulation • May be subject to “herding” or positive-feedback loops
  49. 49. 59 Source: xkcd, Motivations for contribution to public goods
  50. 50. Summary: SNA tells you: • Macro: – What is the “shape” of the crowd? – Are there sub-groups/clusters? • Micro: – Who is at the “center”? – Who is at the “edge”? – Who is the “bridge”?
  51. 51. NodeXL – next steps • Time is of the essence! – Contrast graph A and B – Time series analysis of many “frames” • Keyword networks – “semantic” associations in social media • “The Web” – Browser-based interface to NodeXL • Federated Data Collection – Array of many collectors sharing resulting data
  52. 52. Join the Social Media Research Foundation • Contribute to the NodeXL project – Developers, users, researchers are welcome! • Join the distributed data collection project – Run the data collector and share your results • Apply our tools and data to your research!
  53. 53. What makes it social? • Who makes it? • Who consumes it? • Who owns it?/Who profits from it? • Who or what makes it successful? • How to harness the swarm? • How to map and understand its dynamics? – How do people and group vary? – Who links to whom? • What is next for social media?
  54. 54. Dyadic exchanges. Email to named individual(s) Committee reports to a decision maker/reviewer Professional services reports for decision makers Local email list “Social” blogs Personal social network profile page Multiple authored specialty publications Group blogs. Personal social networks Professional reports to specialty groups Value added economic data Bloomberg Messages to discussion groups/web board Sole authored source code Popular blogs Novels Multiple authored popular media, software Journalism Wikipedia Pages Popular group blogs Collective search engine users Market behavior Query log optimizations Market analysis How large are the social groups producing and consuming social media? Individuals Small Groups Large Groups Individuals Small Groups Large Groups Producers Consumers
  55. 55. Digital Object Editing Granularity Fine (Character/Pixel/Byte) Medium (Object/Attribute/Track/Player) Coarse (Document/Message/Blog Post/Photo) Digital Object Editing Synchronicity Each user can directly control smallest units of content. Each user controls medium sized blocks of content that can only indirectly alter or be altered by other user’s content in a larger shared data structure. Each user controls a block of content, rarely edited or modified by others with only associative linkages. Synchronous Real time Shared canvas Virtual Worlds Multiplayer Games Real-time networked musical jamming Chat, IM, Twitter Asynchronous Shared docs, images, video, audio Source code Wikipedia Contribution to collected works (album, anthology, report section, discussion group, photosets and other collections). Email Blog posts Link sharing Photo sharing Document sharing Turn based games Dimensions of Social Media: How large are the pieces of social media? How interactive is the rate of exchange?
  56. 56. Dimensions of Social Media: Who can exercise what property rights over social media? Author Group of authors Recipients Observers Host Public Domain Types of property rights “What does it mean to own social media content?” Create? Copy/Paste? Edit/Delete? Limit access? Revoke access? Monitor access? Transfer to new host? Transfer rights to others? Commercial exploitation? Adjoining display rights? (can I put ads near your content when I show it to other people)? Aggregation and secondary analysis rights? Who owns social media content?
  57. 57. 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.
  58. 58. 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)
  59. 59. 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
  60. 60. Whyte, William H. 1971. City: Rediscovering the Center. New York: Anchor Books.
  61. 61. "All phones will be smartphones eventually," Sanjay Jha, chief executive of Motorola's mobile phone business said during a recent interview with the Financial Times. Smartphone sales in the US will climb steadily over the next 18 months and account for just under 50 per cent of total sales by the autumn of next year
  62. 62. Auto-Tweet Your Weight to the World By ERIC A. TAUB If you’re losing weight, why keep it to yourself? Now the whole world can know, automatically. With the Wi-Fi Connected Body Scale from Withings, a French company, everyone can know your body weight, lean and fat mass, and your B.M.I., or body mass index. The $159 scale, available from the company’s Web site and, automatically keeps track of up to eight users’ body stats. Step on the scale, and electrical impedance figures out your body fat. It then sends all the information via Wi-Fi to a no-charge Web site, a free iPhone app, Twitter, Google Health and Microsoft HealthVault, among others. The idea is that by amassing a continual flow of data, you’ll be able to monitor your progress in maintaining, or achieving, a healthy life style. The Wi-Fi Connected Body Scale keeps track of your weight in pounds, kilograms or stones (used in Great Britain., one stone equals 14 pounds). The eight users are distinguished by their weight. If you don’t want everyone to see the ups and downs of your avoirdupois, you can always create a Twitter account that only you (and your doctor) know about.
  63. 63. Novartis chip to help ensure bitter pills are swallowed By Andrew Jack in London Published: 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.
  64. 64. 74
  65. 65. Trace Encounters:
  66. 66. Poken makes social exchanges simple and cheap:
  67. 67. FitBit consumer activity monitoring.
  68. 68.
  69. 69. WIFE/MOTHER/WORKER/SPY Does This Pencil Skirt Have an App? “…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
  70. 70. 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.
  71. 71. CureTogether: Cure Together People aggregate their self-generated medical data!
  72. 72. 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. /18/the-future-of-helath-insurance- mobile-medical-sensors-and-dynamic- pricing/
  73. 73. Prediction: a mobile App will be more medically effective than many drugs If only because it will make you take the drug properly
  74. 74. A project from the Social Media Research Foundation: Smart Society and Civic Culture