Embed Size (px)
Citation preview
Social Media Analytics:
how to “think link”
Marc A. SmithChief Social ScientistSocial Media Research Foundationhttp://smrfoundation.org http://nodexl.codeplex.com/http://nodexlgraphgallery.org
A project from the Social Media Research Foundation: http://www.smrfoundation.org
About Me
Introductions
Marc A. SmithChief Social Scientist / DirectorSocial Media Research Foundation
[email protected] http://www.smrfoundation.orghttp://www.codeplex.com/nodexlhttp://www.twitter.com/marc_smithhttp://www.linkedin.com/in/marcasmithhttp://www.slideshare.net/Marc_A_Smithhttp://www.flickr.com/photos/marc_smithhttp://www.facebook.com/marc.smith.sociologist
Crowds matter
http://www.flickr.com/photos/amycgx/3119640267/
Crowds in social media matter
Crowds in social media have a hidden structure
https://demo-3dg-viz.herokuapp.com/
Kodak BrownieSnap-Shot Camera
The first easy to use
point and shoot!
NodeXL Ribbon in Excel
NodeXL in Excel
#socbiz Twitter NodeXL SNA Map and Report for Tuesday, 15 September 2015 at 14:43 UTC
Broadcast
Broadcast
Brand(Isolates)
Broadcast
Broadcast
Broadcast
Broadcast
Broadcast
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=53137#headerTopVertices
Are you the next mayor of
#MMeasure?
Tweet!
Request a sample social media network map
for the topic of your choice:
http://bit.ly/1J7waPY
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/
[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
http://www.pewresearch.org/fact-tank/2014/02/20/the-six-types-of-twitter-conversations/
[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
custexp Twitter NodeXL SNA Map and Report for Tuesday, 25 August 2015 at 19:18 UTC
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=52355#headerTopVertices
https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=52355#headerTopHashtags
“Think Link”Nodes & Edges
Is related to
A BIs related to
Is related to
“Think Link”Using Nodes & Edges to
find people in the “middle of things”
A BIs related to
CIs related to
Who is the “mayor” of your hashtag?• How to measure “influence” in social media?• Influence is a property of the network, not the
individual. • It is a location in the network where messages tend
to be repeated.• Network measures of “centrality” can be applied to
find “influential” people in social media.
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.
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
NodeXL imports “edges” from social media data sources
World Wide Web
Social media must contain one or more
social networks
Crowds in social media form networks
Social Media (email, Facebook, Twitter, YouTube, and more) is all about connections
from people
to people.
41
There are many kinds of ties…. Send, Mention,
http://www.flickr.com/photos/stevendepolo/3254238329
Like, Link, Reply, Rate, Review, Favorite, Friend, Follow, Forward, Edit, Tag, Comment, Check-in…
Internet Verbs!
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.
Patterns are left behind
44
• 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 Theoryhttp://en.wikipedia.org/wiki/Social_network
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
CD
E
A B D E
http://www.bonkersworld.net/organizational-charts/
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
Now Available
Communities in Cyberspace
Network Analysis Data Flow
PublicationVisualizationAnalysisContainerProviders
Social Network Maps Reveal
Key influencers in any topic.
Sub-groups.
Bridges.
Hubs
Bridges
Islands
http://www.flickr.com/photos/storm-crypt/3047698741
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?
[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
Your social media audience is smaller…
…than the audiences of ten influential voices.
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.
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
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.
Speak the language of the mayorsThe “perfect” tweet:
.@Theirname #Theirhashtag News about your brand using their words http://your.site #Yourhashtag
Speak the language of the mayors
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!
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
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)
[Divided]Polarized Crowds
[Unified]Tight Crowd
[Fragmented]Brand Clusters
[Clustered]Communities
[In-Hub & Spoke]Broadcast Network
[Out-Hub & Spoke]Support Network
[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.
Request your own network map and report
http://connectedaction.net
Monitor your topics with social network maps• Identify the
• Key people• Groups• Top topics
• Locate your social media accounts within the network
Social Media Analytics:
how to “think link”
Marc A. SmithChief Social ScientistSocial Media Research Foundationhttp://smrfoundation.org http://nodexl.codeplex.com/http://nodexlgraphgallery.org
A project from the Social Media Research Foundation: http://www.smrfoundation.org
