Social Media Network & Information Professionals

Transcription

1 Natasa Milic-Frayling, Microsoft Research Ben Shneiderman, Univ. of Maryland Marc A. Smith, Connected Action

2 Input devices & strategies Keyboards, pointing devices, voice Direct manipulation Menus, forms, commands Output devices & formats Screens, windows, color, sound Text, tables, graphics Instructions, messages, help Collaboration & Social Media Help, tutorials, training Search Visualization Fifth Edition: 2010

3 1) E-commerce & National Priorities Customer loyalty, community formation Disaster response, community safety Health, energy, education, e-government 2) Develop Theories of Social Participation How do social media networks evolve? How can participation be increased? 3) Provide Technology Infrastructure Scalable, reliable, universal, manageable Protect privacy, stop attacks, resolve conflicts

4 1) E-commerce & National Priorities Customer loyalty, community formation Disaster response, community safety Health, energy, education, e-government 2) Develop Theories of Social Participation How do social media networks evolve? How can participation be increased? 3) Provide Technology Infrastructure Scalable, reliable, universal, manageable Protect privacy, stop attacks, resolve conflicts

5 Informal Gathering College Park, MD, April 2009 Article: Science March 2009 BEN SHNEIDERMAN

6 NSF Workshops: Palo Alto & DC

7 Community Informatics Research Network intlsocialparticipation.net

8 E-Commerce Social Media

9 911.gov Residents report information Professionals disseminate instructions Resident-to-Resident assistance Sending SMS message to 911, includes your phone number, location and time Shneiderman & Preece, Science (Feb. 16, 2007)

10 911.gov Residents report information Professionals disseminate instructions Resident-to-Resident assistance Amber Alert Sending SMS message to 911, includes your phone number, location and time Shneiderman & Preece, Science (Feb. 16, 2007)

11 911.gov Residents report information Professionals disseminate instructions Resident-to-Resident assistance Amber Alert Sending SMS message to 911, includes your phone number, location and time Shneiderman & Preece, Science (Feb. 16, 2007)

12 Health, Energy, Education,

13 Health, Energy, Education,

14 Health, Energy, Education,

15 1) E-commerce & National Priorities Customer loyalty, community formation Disaster response, community safety Health, energy, education, e-government 2) Develop Theories of Social Participation How do social media networks evolve? How can participation be increased? 3) Provide Technology Infrastructure Scalable, reliable, universal, manageable Protect privacy, stop attacks, resolve conflicts

16 Network Theories: Evolution models Random, preferential attachment, Monotonic, bursty, Power law for degree (hubs & indexes) Small-world property Forest fire, spreading activation, Matures, decays, fragments, Watts & Strogatz, Nature 1998; Barabasi, Science 1999, 2009; Newman, Phys. Rev. Letters 2002 Kumar, Novak & Tomkins, KDD2006 Leskovec, Faloutsos & Kleinberg, TKDD2007

17 Network Theories: Social science Relationships & roles Strong & weak ties Motivations: egoism, altruism, collectivism, principlism Collective intelligence & action Leadership & governance Social information foraging Moreno, 1938; Granovetter, 1971; Burt, 1987; Ostrom, 1992; Wellman, 1993; Batson, Ahmad & Tseng, 2002; Malone, Laubaucher & Dellarocas, 2009; Pirolli, 2009

18 Network Theories: Stages of participation Wikipedia, Discussion & Reporting Reader First-time Contributor (Legitimate Peripheral Participation) Returning Contributor Frequent Contributor Preece, Nonnecke & Andrews, CHB2004 Forte & Bruckman, SIGGROUP2005; Hanson, 2008 Porter: Designing for the Social Web, 2008 Vassileva, 2002, 2005; Ling et al., JCMC 2005; Rashid et al., CHI2006

19 From Reader to Leader: Motivating Technology-Mediated Social Participation All Users Reader Contributor Collaborator ` Leader Preece & Shneiderman, AIS Trans. Human-Computer Interaction1 (1), 2009 aisel.aisnet.org/thci/vol1/iss1/5/

20 1) E-commerce & National Priorities Customer loyalty, community formation Disaster response, community safety Health, energy, education, e-government 2) Develop Theories of Social Participation How do social media networks evolve? How can participation be increased? 3) Provide Technology Infrastructure Scalable, reliable, universal, manageable Protect privacy, stop attacks, resolve conflicts

21 Mobile, Desktop, Web, Cloud 100% uptime, 100% secure Giga-collabs, Tera-contribs Universal accessibility & usability Trust, empathy, responsibility, privacy Leaders can manage usage Designers can continuously improve

22 Footprints of Human Activity Footprints in sand as Caesarea

23 Preparation Own the problem & define the schedule Data cleaning & conditioning Handle missing & uncertain data Extract subsets & link to related information

24 Integrates statistics & visualization 4 case studies, 4-8 weeks (journalist, bibliometrician, terrorist analyst & organizational analyst) Identified desired features, gave strong positive feedback about benefits of integration Perer & Shneiderman, CHI2008, IEEE CG&A 2009

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32 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 Threaded Networks 10. Twitter 11. Facebook 12. WWW 13. Flickr 14. YouTube 15. Wiki Networks

33 Challenge: Requires Partitioning Easy : Only need locally connected vertices e.g Vertex Degree, Eigenvector centrality Relatively Hard : Need local & some global graph knowledge e.g. Fruchterman-Reingold layout Hard : Need global graph knowledge at each node e.g. all pairs shortest paths -> betweenness centrality Udayan Khurana

34 Implement and Measure Performance for Fruchterman-Reingold Layout Algorithm GPU Host CPU GeForce GTX 285, 1476 MHz, 240 cores 3 GHz, Intel(R) Core(TM)2 Duo Graph Name #Nodes #Edges F-R run time (seconds) CA-AstroPh 18, , CUDA F-R run time (seconds) cit-hepph 34, , soc-epinions1 75, , soc-slashdot , , soc-slashdot , , John Locke Max Scharrenbroich Puneet Sharma Graphs from STANFORD S SNAP Library (

35 Researchers who want to - create open tools - generate & host open data - support open scholarship Map, measure & understand social media Support tool projects to collection, analyze & visualize social media data.

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37 THANKS!!! to Microsoft External Research

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41 Location, Location, Location

42 Network of connections among ecomm mentioning Twitter users ecomm Position, Position, Position

43 History: from the dawn of time! Theory and method: > Jacob L. Moreno dia.org/wiki/jac ob_l._moreno

44 SNA 101 B D F H A E I C G 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 C Central connector above average centrality Broker above average betweenness A B D E E D

45 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 Source: Richards, W. (1986). The NEGOPY network analysis program. Burnaby, BC: Department of Communication, Simon Fraser University. pp.7-16 (Hampton &Wellman, 1999; Paolillo, 2001; Wellman, 2001)y

46 Degree Closeness Betweenness Eigenvector

47 Social Media Network Roles Welser, Howard T., Eric Gleave, Danyel Fisher, and Marc Smith Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). [Local copy] Experts and Answer People Discussion people, Topic setters Discussion starters, Topic setters

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49 Leverage spreadsheet for storage of edge and vertex data

50 Social Media Research Foundation Open Tools Open Data Open Scholarship

51 A minimal network can illustrate the ways different locations have different values for centrality and degree

52 Forthcoming, August 2010

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54 Import from multiple social media network sources

55 Social Media Research Foundation

56 #facsumm at 9:30 AM Monday, July 12, 2010

57 #facsumm at 2:30 PM Monday, July 12, 2010

58 #microsoftresearch at 1:15 PM Monday, July 12, 2010

59 Microsoft at 6:00 AM Monday, July 12, 2010

60 Microsoft at 6:00 AM Monday, July 12, 2010

61 Bing at 2:30 AM Monday, July 12, 2010

62 GOP June 13, 2010 at 5:30PM

63 teaparty at 1:00PM April 14, 2010

64 Global Warming at 6:00 PM Monday, May 7, 2010

65 Global Warming at 5:30 PM Monday, May 7, 2010

66 WWW2010 at 10:30 PM Monday, April 28, 2010

67 WWW2010 at 10:30 PM Monday, April 28, 2010

68 April NodeXL - Twitter - CHI2010 X Log of Followers Y Log of Tweets

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70 May NodeXL - twitter global warming

71 May NodeXL - twitter climate change

72 Social Media Research Foundation

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