A  project  from  the  Social  Media  Research  Founda8on:  h:p://www.smrfounda8on.org  

About  Me  

Introduc8ons    Marc  A.  Smith  Chief  Social  Scien8st  Connected  Ac8on  Consul8ng  Group    Marc@connectedac8on.net  h:p://www.connectedac8on.net  h:p://www.codeplex.com/nodexl  h:p://www.twi:er.com/marc_smith  h:p://delicious.com/marc_smith/Paper    h:p://www.flickr.com/photos/marc_smith  h:p://www.facebook.com/marc.smith.sociologist  h:p://www.linkedin.com/in/marcasmith  h:p://www.slideshare.net/Marc_A_Smith  h:p://www.smrfounda8on.org    

Social  Media  Research  Founda8on  h"p://smrfounda/on.org  

Social  Media    (email,  Facebook,  Twi:er,  YouTube,  and  more)    is  all  about    connec8ons    

 from  people        

     to  people.  

4  

Pa:erns  are                      leO  behind  

5  

There  are  many  kinds  of  8es….    Send,  Men8on,    

h:p://www.flickr.com/photos/stevendepolo/3254238329  

Like,  Link,  Reply,  Rate,  Review,  Favorite,  Friend,  Follow,  Forward,  Edit,  Tag,  Comment,  Check-­‐in…    

Strong  (es  

Weak  (es  

h:p://www.flickr.com/photos/fullaperture/81266869/  

Strength  of  Weak  (es  

“Think  Link”  Nodes  &  Edges  

Is  related  to  

A   B  

World  Wide  Web  

Each  contains  one  or  more  social  networks  

Vertex1   Vertex  2   “Edge”  A9ribute  

“Vertex1”  A9ribute  

“Vertex2”  A9ribute  

@UserName1   @UserName2   value   value   value  

A  network  is  born  whenever  two  GUIDs  are  joined.  

Username   A9ributes  

@UserName1   Value,  value  

Username   A9ributes  

@UserName2   Value,  value  

A   B  

NodeXL  imports  “edges”  from  social  media  data  sources  

Social    Networks  

•  History:  from  the  dawn  of  •  History:  8me!  

from  the  dawn  of  8me!  

•  Theory  and  

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 Jacob Moreno’s early social network diagram of positive and negative relationships among members of a Publishing Company.

A nearly social network diagram of relationships among workers in a factory illustrates the positions different workers occupy within the workgroup.

Originally published in Roethlisberger, F., and Dickson, W. (1939). Management and the worker. Cambridge, UK: Cambridge University Press.

Loca8on,  Loca8on,  Loca8on  

Posi8on,  Posi8on,  Posi8on  

Introduc8on  to  NodeXL  

Like  MSPaint™  for  graphs.  —  the  Community  

Now  Available  

Communi8es  in  Cyberspace  

h:p://www.flickr.com/photos/badgopher/3264760070/  

h:p://www.flickr.com/photos/druclimb/2212572259/in/photostream/  

h:p://www.flickr.com/photos/hchalkley/47839243/  

h:p://www.flickr.com/photos/rvwith8to/4236716778  

h:p://www.flickr.com/photos/62693815@N03/6277208708/  

Social  Network  Maps  Reveal  

 Key  influencers  in  any  topic.  

 Sub-­‐groups.  

 Bridges.  

 

Hubs  

Bridges  

h:p://www.flickr.com/photos/storm-­‐crypt/3047698741  

h:p://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/  

h:p://www.flickr.com/photos/amycgx/3119640267/  

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  se:ers  

Discussion  people,  Topic  se:ers  

Heather  has  high  

betweenness  

NodeXL  Network  Overview  Discovery  and  Explora(on  add-­‐in  for  Excel  2007/2010  

A  minimal  network  can  illustrate  the  ways  different  

loca8ons  have  different  values  for  centrality  and  degree  

#occupywallstreet  15  November  2011  

#teaparty  15  November  2011  

h:p://www.newscien8st.com/blogs/onepercent/2011/11/occupy-­‐vs-­‐tea-­‐party-­‐what-­‐their.html  

6  kinds  of  Twi:er  social  media  networks  

#My2K  

Polarized  

#CMgrChat  

In-­‐group  /  Community  

Lumia  

Brand  /  Public  Topic  

#FLOTUS  

Bazaar  

New  York  Times  Ar8cle  Paul  Krugman  

Broadcast:  Audience  +  Communi8es  

Dell  Listens/Dellcares  

Support  

SNA  ques8ons  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  #PAWCON  look  like?  

h:p://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=4110  

h:p://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=4113  

h:p://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=3982  

Top  10  Ver8ces,  Ranked  by  Betweenness  Centrality:  @SQLServer  @eric_kavanagh  @DBA_MAN  @confio  @DZone  @SQLRockstar  @YvesMulkers  @BrentO  @SQL_By_Joey  @zymasesystems  

h:p://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=4114  

h:p://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=4111  

•  Central  tenet      –  Social  structure  emerges  from    –    the  aggregate  of  rela8onships  (8es)    –    among  members  of  a  popula8on  

•  Phenomena  of  interest  –  Emergence  of  cliques  and  clusters    –    from  pa:erns  of  rela8onships  –  Centrality  (core),  periphery  (isolates),    –    betweenness  

•  Methods  –  Surveys,  interviews,  observa8ons,    

log  file  analysis,  computa8onal    analysis  of  matrices    

(Hampton  &Wellman,  1999;  Paolillo,  2001;  Wellman,  2001)  

Source:  Richards,  W.  (1986).  The  NEGOPY  network  analysis  program.  Burnaby,  BC:  Department  of    Communica8on,  Simon  Fraser  University.  pp.7-­‐16

Social  Network  Theory  h:p://en.wikipedia.org/wiki/Social_network  

SNA  101  •  Node  

–  “actor”  on  which  rela8onships  act;  1-­‐mode  versus  2-­‐mode  networks  •  Edge  

–  Rela8onship  connec8ng  nodes;  can  be  direc8onal  •  Cohesive  Sub-­‐Group  

–  Well-­‐connected  group;  clique;  cluster  •  Key  Metrics  

–  Centrality  (group  or  individual  measure)  •  Number  of  direct  connec8ons  that  individuals  have  with  others  in  the  group  (usually  look  at  incoming  connec8ons  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  connec8ons  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  

C  B  

H  

G  

I  

C  D  

E  

A   B   D   E  

NodeXL Free/Open Social Network Analysis add-in for Excel 2007/2010 makes graph

theory as easy as a pie chart, with integrated analysis of social media sources. http://nodexl.codeplex.com

h:p://www.youtube.com/watch?v=0M3T65Iw3Ac  

Nod

eXL  Vide

o  

Goal:  Make  SNA  easier  

•  Exis8ng  Social  Network  Tools  are  challenging  for  many  novice  users  

•  Tools  like  Excel  are  widely  used  •  Leveraging  a  spreadsheet  as  a  host  for  SNA  lowers  barriers  to  network  data  analysis  and  display  

Twitter Network for “Microsoft Research” *BEFORE*

Twitter Network for “Microsoft Research” *AFTER*

Network  Mo8f  Simplifica8on  

Cody  Dunne,  University  of  Maryland  

NodeXL  Graph  Gallery  

NodeXL  calculates  network  metrics  and  

word  pairs  

!

The  Content  summary  spreadsheet  displays  the  most  frequently  used  URLs,  hashtags,  

and  user  names  within  the  network  as  a  whole  and  within  each  calculated  sub-­‐group.    

!

NodeXL  Ribbon  in  Excel  

NodeXL  data  import  sources  

Example  NodeXL  data  importer  for  Twi:er  

NodeXL  imports  “edges”  from  social  media  data  sources  

NodeXL  creates  a  list  of  “ver8ces”  from  imported  social  media  edges  

NodeXL  displays  subgraph  images  along  with  network  metadata  

NodeXL      Automa8on    

makes  analysis    simple  and  fast  

Perform  collec8ons  of  common  

opera8ons  with  a  single  click  

NodeXL  Generates  Overall  Network  Metrics  

Social  Media  Research  Founda8on  People   Disciplines   Ins(tu(ons  

University  Faculty  

Computer  Science   University  of  Maryland  

Students   HCI,  CSCW   Oxford  Internet  Ins8tute  

Industry     Machine  Learning   Stanford  University  

Independent     Informa8on  Visualiza8on  

MicrosoO  Research  

Researchers     UI/UX   Illinois  Ins8tute  of  Technology  

Developers     Social  Science/Sociology   Connected  Ac8on  

Network  Analysis     Cornell  

Collec8ve  Ac8on     Morningside  Analy8cs  

What  we  are  trying  to  do:  Open  Tools,  Open  Data,  Open  Scholarship  •  Build  the  “Firefox  of  GraphML”  –  open  tools  for  collec8ng  and  visualizing  social  media  data  

•  Connect  users  to  network  analysis  –  make    network  charts  as  easy  as  making  a  pie  chart  

•  Connect  researchers  to  social  media  data  sources  •  Archive:  Be  the  “Allen  Very  Large  Telescope  Array”  for  Social  Media  data  –  coordinate  and  aggregate  the  results  of  many  user’s  data  collec8on  and  analysis  

•  Create  open  access  research  papers  &  findings  •  Make  “collec3ons  of  connec3ons”  easy  for  users  to  manage    

What  we  have  done:  Open  Tools  

•  NodeXL  •  Data  providers  (“spigots”)  

–  ThreadMill  Message  Board  –  Exchange  Enterprise  Email  –  Voson  Hyperlink  –  SharePoint  –  Facebook  –  Twi:er  –  YouTube  –  Flickr  

What  we  have  done:  Open  Data  •  NodeXLGraphGallery.org  

– User  generated  collec8on  of  network  graphs,  datasets  and  annota8ons  

–  Collec8ve  repository  for  the  research  community  

–  Published  collec8ons  of  data  from  a  range  of  social  media  data  sources  to  help  students  and  researchers  connect  with  data  of  interest  and  relevance  

What  we  have  done:  Open  Scholarship  

What  we  have  done:  Open  Scholarship  

What  we  want  to  do:    (Build  the  tools  to)  map  the  social  web  •  Move  NodeXL  to  the  web:  (Node[NOT]XL)  

–  Node  for  Google  Doc  Spreadsheets?    –  WebGL  Canvas?  D3.JS?  Sigma.JS  

•  Connect  to  more  data  sources  of  interest:  –  RDF,  MediaWikis,  Gmail,  NYT,  Cita8on  Networks  

•  Solve  hard  network  manipula8on  UI  problems:  –  Modal  transform,  Time  series,  Automated  layouts  

•  Grow  and  maintain  archives  of  social  media  network  data  sets  for  research  use.  

•  Improve  network  science  educa8on:  –  Workshops  on  social  media  network  analysis  –  Live  lectures  and  presenta8ons  –  Videos  and  training  materials  

How  you  can  help  

•  Sponsor  a  feature  •  Sponsor  workshops  •  Sponsor  a  student  •  Schedule  training  •  Sponsor  the  founda8on  •  Donate  your  money,  code,  computa8on,  storage,  bandwidth,  data  or  employee’s  8me  

•  Help  promote  the  work  of  the  Social  Media  Research  Founda8on  

Who  is  the  mayor  of  your  hashtag?              

Find  out  at:  h:p://netbadges.com  

Who  is  the  mayor  of  your  hashtag?  

Find  out  at:  h"p://netbadges.com  

A  project  from  the  Social  Media  Research  Founda8on:  h:p://www.smrfounda8on.org