Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
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Marc Smith discusses social network analysis of social media data. He outlines six types of social media networks: polarized crowds, tight crowds, brand clusters, community clusters, broadcast networks, and support networks. Each network type reflects different social dynamics. Smith advocates using social network mapping tools like NodeXL to analyze topics on social media to identify influential users, subgroups, and opportunities for shaping conversations.
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Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL
- 1. Marc Smith Charting Collections of Connections in Social Media EMERGENCE Speaker 9 of 17 Followed by Original Swimming Party @marc_smith
- 3. Speaker 1 of 15 Followed by Name Surname About Me Marc A. Smith Chief Social Scientist / Director Social Media Research Foundation marc@smrfoundation.org http://www.smrfoundation.org http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist
- 5. http://www.flickr.com/photos/amycgx/3119640267/ Crowds in social media matter
- 6. Crowds in social media have a hidden structure
- 7. 7
- 8. 8
- 9. The first easy to use point and shoot camera! Kodak Brownie Snap- Shot Camera
- 10. We envision hundreds of NodeXL data collectors around the world collectively generating a free and open archive of social media network snapshots on a wide range of topics. http://msnbcmedia.msn.com/i/msnbc/Components/Photos/ 071012/071012_telescope_hmed_3p.jpg
- 11. World Wide Web Social media must contain one or more social networks Crowds in social media form networks
- 12. Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections from people to people. 12
- 13. There are many kinds of ties…. http://www.flickr.com/photos/stevendepolo/3254238329 Send, Mention, Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
- 15. “Think Link”Nodes & Edges Is related to A B Is related to Is related to
- 16. “Think Link”Nodes & Edges A B Is related to Is related to Is related to
- 17. Vertex1 Vertex 2 “Edge” Attribute “Vertex1” Attribute “Vertex2” Attribute @UserName1 @UserName2 value value value A network is born whenever two GUIDs are joined. Username Attributes @UserName1 Value, value Username Attributes @UserName2 Value, value A B
- 18. NodeXL imports “edges” from many social media data sources
- 19. Social media network analysis • Social media is inherently made of networks, – which are created when people link and reply. • Collections of connections have an emergent shape, – Some shapes are better than others. • Some people are located in strategic locations in these shapes, – Centrally located people are more influential than others.
- 20. • 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 CommunicaNon, Simon Fraser University. pp. 7-‐16 Social Network Theory http://en.wikipedia.org/wiki/Social_network
- 21. • Node – “actor” on which relaNonships act; 1-‐mode versus 2-‐mode networks • Edge – RelaNonship connecNng nodes; can be direcNonal • Cohesive Sub-‐Group – Well-‐connected group; clique; cluster • Key Metrics – Centrality (group or individual measure) • Number of direct connecNons that individuals have with others in the group (usually look at incoming connecNons 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 connecNons 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 101 E D F A C B H G I C D E A B D E
- 22. Welser, Howard T., Eric Gleave, Danyel Fisher, & Marc Smith. 2007. Visualizing the Signatures of Social Roles in Online Discussion Groups. The Journal of Social Structure. 8(2). Experts & “Answer People” Discussion starters Topic setters Discussion people
- 25. Social Networks • History: from the dawn of time! • Theory and method: 1932 -> • Jacob L. Moreno http://en.wikipedia.org/ wiki/Jacob_L._Moreno Jacob Moreno’s early social network diagram of positive and negative relationships among members of a football team. Originally published in Moreno, J. L. (1934). Who shall survive? Washington, DC: Nervous and Mental Disease Publishing Company.
- 28. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
- 29. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
- 31. Now Available
- 34. Social Network Maps Reveal Key influencers in any topic. Sub-groups. Bridges.
- 35. Hubs
- 36. Bridges
- 38. SNA questions for social media: 1. What does my topic network look like? 2. What does the topic I aspire to be look like? 3. What is the difference between #1 and #2? 4. How does my map change as I intervene? What does #YourHashtag look like? Who is the mayor of #YourHashtag?
- 39. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Community Clusters [In-Hub & Spoke] Broadcast Network [Out-Hub & Spoke] Support Network 6 kinds of Twitter social media networks
- 40. Your social media audience is smaller… …than the audiences of ten influential voices.
- 41. The “mayor” of your hashtag • Some people are at the center of the conversation • “Centrality” is about being in the middle of the discussion – Not “Followers” – Not “Tweets” – Not “RTs” – Not “Mentions” • The “mayor” has an audience that may be bigger than yours.
- 42. Build a collection of mayors • Map multiple topics – Your brand and company names – Your competitor brands and company names – The names of the activities or locations related to your products • Identify the top people in each topic • Follow these people – 30-50% of the time they follow you back • Re-tweet these people (if they did not follow you) • 30-50% of the time they follow you back
- 43. Speak the language of the mayors • Use NodeXL content analysis to identify each users most salient: – Words – Word pairs – URLs – #Hashtags • Mix the language of the Mayors with your brand’s messages.
- 44. Speak the language of the mayors Ø The “perfect” tweet: .@Theirname #Theirhashtag News about your brand using their words http://your.site #Yourhashtag
- 45. Speak the language of the mayors
- 46. Some shapes are better than others: • The value of Broadcast versus community network! • From community to brand! • Support and why community can be a signal of failure!
- 47. Three network phases of social media success Phase 1: You get an audience Phase 2: Your audience gets an audience Phase 3: Audience becomes community
- 48. Some shapes are better than others • Each shape reflects the kind of social activity that generates it: – Divided: Conflict – Unified: In-group – Brand: Fragmentation – Community: Clustering – Broadcast: Hub and spoke (In) – Support: Hub and spoke (Out)
- 49. [Divided] Polarized Crowds [Unified] Tight Crowd [Fragmented] Brand Clusters [Clustered] Communi8es [In-‐Hub & Spoke] Broadcast [Out-‐Hub & Spoke] Support [Low probability] Find bridge users. Encourage shared material. [Low probability] Get message out to disconnected communities. [Possible transition] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Remove bridges, highlight divisions. [Low probability] Get message out to disconnected communities. [High probability] Draw in new participants. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [High probability] Increase retention, build connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Undesirable transition] Increase population, reduce connections. [Possible transition] Regularly create content. [Possible transition] Reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Low probability] Get message out to disconnected communities. [Possible transition] Increase retention, build connections. [High probability] Increase reply rate, reply to multiple users. [Undesirable transition] Increase density of connections in two groups. [Low probability] Dramatically increase density of connections. [Possible transition] Get message out to disconnected communities. [High probability] Increase retention, build connections. [High probability] Increase publication of new content and regularly create content.
- 50. Request your own network map and report http://connectedaction.net
- 51. Monitor your topics with social network maps • Identify the – Key people – Groups – Top topics • Locate your social media accounts within the network
- 52. How you can help • Sponsor a feature • Sponsor workshops • Sponsor a student • Schedule training • Sponsor the foundation • Donate your money, code, computation, storage, bandwidth, data or employee’s time • Help promote the work of the Social Media Research Foundation