Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL

Marc Smith

Charting Collections of Connections in Social Media

EMERGENCE Speaker 9 of 17

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@marc_smith

A project from the Social Media Research Foundation: http://www.smrfoundation.org

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About Me Marc A. Smith Chief Social Scientist / Director Social Media Research Foundation [email protected] 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

Crowds matter

http://www.flickr.com/photos/amycgx/3119640267/

Crowds in social media matter

Crowds in social media have a hidden structure

7

8

The first easy to use

point and shoot camera!

Kodak Brownie

Snap- Shot

Camera

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

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.

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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…

Patterns are left behind

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“Think Link” Nodes & Edges

Is related to

A B

Is related to

Is related to

“Think Link” Nodes & Edges

A B

Is related to

Is related to

Is related to

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 many social media data sources

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.

•  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

•  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

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

Mapping MammothBI in Twitter

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.

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/

Now Available

Communities in Cyberspace

Social Network Maps Reveal

Key influencers in any topic.

Sub-groups.

Bridges.

Hubs

Bridges

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?








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.


Ø The “perfect” tweet:

.@Theirname #Theirhashtag News about your brand using their words http://your.site #Yourhashtag


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)



[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.


[High probability] Draw in new participants.



[Undesirable transition] Increase density of connections in two groups.

[Low probability] Dramatically increase density of connections.

[High probability] Increase retention, build connections.





[Undesirable transition] Increase population, reduce connections.






[Possible transition] Increase retention, build connections.

[High probability] Increase reply rate, reply to multiple users.



[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

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

Data & Analytics

Marc Smith - Charting Collections of Connections in Social Media: Creating Maps and Measures with NodeXL