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1 Running Head: Student athletes and the influence of social media Persuasion by Centrality: A Social Network Analysis of 2016 UVA Football Recruits Billy Skrobacz 05/06/13

Persuasion by Centrality_SNA

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Running  Head:  Student  athletes  and  the  influence  of  social  media            

Persuasion  by  Centrality:  A  Social  Network  Analysis  of  2016  UVA  Football  Recruits  

Billy  Skrobacz  05/06/13  

                                                         

             

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Intro:    In  a  world  of  e-­‐bills,  e-­‐mail,  and  dying  newspapers,  the  increasing  prevalence  

of  Internet  profiles  in  the  public  eye  is  unavoidable.  For  example,  not  only  are  

people  associating  themselves  with  an  online  identity  known  as  a  “twitter  handle,”  

but  they  are  also  making  these  virtual  identities  public,  sharing  some  of  their  most  

personal,  and  in  some  cases  extreme  opinions  and  beliefs  with  their  friends,  

acquaintances,  and  followers.  The  share  of  such  personal  content  through  online  

identities  is  elevated  to  a  whole  new  level  when  the  people  behind  the  “handles”  are  

future  NCAA  student  athletes.  Not  only  are  these  athletes  already  under  the  strict  

microscope  of  the  public  eye  for  their  work  on  the  field  or  court,  but  also  their  use  of  

social  media  creates  full-­‐time  accessibility  from  people  who  are  not  in  their  

immediate  circle  of  friends  and  family.  This  easily  accessible  and  opinionated  

content,  created  by  the  use  of  social  media,  plays  a  pivotal  role  in  the  persuasive  

influence  that  student  athletes  have  over  their  peers.  

While  intercollegiate  student  athletes  have  a  governing  body  in  the  NCAA  

that  monitors  current  team  and  player  accounts  for  punishable  content,  this  

authority  has  no  control  over  prospective  high  school  student  athletes.  So,  though  it  

is  illegal  for  a  current  NCAA  athlete  to  directly  contact  a  potential  recruit  via  social  

media,  the  NCAA  has  no  control  over  recruits  contacting  other  potential  prospects  

throughout  the  college  recruitment  process.  Therefore,  thanks  to  social  media,  high  

school  athletes  have  all  of  the  accessibility  tools  that  they  need  in  order  to  begin  

recruiting  and  persuading  their  peers  into  making  a  college  decision.  While  the  

mode  of  communication  used  by  today’s  prospective  athletes  is  unique,  this  process  

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of  peer-­‐recruiting  is  not,  and  frequently  happens  in  revenue  sports  where  signing  

recruits  is  a  high-­‐stakes  business  for  coaches  and  athletics  departments.  Therefore,  

when  coaches  begin  recruiting  potential  prospects,  they  will  often  look  at  that  

prospect’s  immediate  circle  of  friends.  This  search,  depending  on  how  sought-­‐after  

the  prospect  is,  occasionally  results  in  a  scholarship  offer  for  the  less  reputable  

friend  with  hopes  that  this  can  persuade  the  more  coveted  athlete  to  attend  their  

institution  as  well.  The  best  example  of  this  can  be  found  in  the  recruiting  class  of  

the  2012  University  of  Georgia  football  team,  where  Quayvon  Hicks  was  offered  a  

scholarship  in  order  to  lock  down  a  commit  from  a  high  school  teammate  and  friend,  

Isaiah  Crowell  (Towers,  2011).  Though  this  worked  out  for  UGA  as  Hicks  became  a  

highly  touted  player,  coaches  more  frequently  find  themselves  wasting  a  scholarship  

with  the  hopes  of  persuasion.    

This  study,  however,  seeks  to  save  coaches  from  wasting  these  valuable  

scholarships,  by  finding  the  most  persuasive  connections  through  a  social  network  

analysis  of  prospective  athletes’  Twitter  accounts.  If  coaches  can  use  this  analysis  to  

find  out  which  recruits  are  the  most  popular  amongst  the  rest  of  the  recruiting  class,  

then  they  should  be  able  to  focus  more  energy  on  getting  that  popular  athlete  to  

commit,  and  ultimately  reap  the  benefits  of  this  athlete’s  influence  over  his  or  her  

athletic  peers.  While  this  study  will  not  be  a  content  analysis  of  prospect’s  Tweets,  it  

will  analyze  athletes’  followers  in  order  to  find  and  exemplify  the  strength  of  the  

central  nodes  of  communication  within  a  particular  recruiting  class.  This  

identification  will  paint  a  picture  for  coaches  and  athletic  administration  as  to  which  

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prospects  are  worthy  of  more  time  on  the  recruiting  trail,  and  which  prospects  can  

be  more  easily  obtained  through  peer  interaction  via  social  media  activity.  

 Review  of  Literature:    

The  growing  prevalence  of  social  media  use  comes  with  many  unexpected  

full-­‐time  implications  for  the  student  athletes  who  are  logged  in  to  these  various  

web  based  sites.  For  the  purposes  of  this  study,  social  media  is  defined  as  “a  group  

of  Internet-­‐based  applications  […]  that  allow  the  creation  and  exchange  of  User  

Generated  Content  (UGC)”  (Kaplan  &  Haenlein,  pg.  61,  2010).    This  content  is  

continually  modified  by  the  user,  and  is  presented  within  a  framework  where  all  

users  have  equality  in  sharing  information  (Clavio,  pg.  310,  2011).  Equality  here  

refers  to  the  universal  ability  for  members  of  a  particular  social  media  network  to,  

both,  produce  and  access  to  information.    

UGC  can  be  analyzed  through  two  frames.    While  a  content-­‐based  frame  

pertains  to  actual  messages  generated  by  users,  an  audience-­‐based  frame  applies  to  

who  receives  these  virtual  stimuli  (Clavio,  2008).  Though  recruiting  prospective  

athletes  relies  partly  on  the  content  and  information  that  a  coach  or  peer  recruiter  

provides,  actual  content,  or  individual  Tweets  produced  by  the  sample  population,  

does  not  fall  within  the  realm  of  research  for  this  particular  study.  Instead,  audience  

based  inquiries  will  be  examined  in  order  to  provide  data  on  which  fellow  recruits  

are  receiving  particular  athletes’  UGC,  and  what  type  of  uses,  gratifications,  and  

general  reactions  are  experienced  from  consumers  of  this  content  (Clavio,  2011).  

Uses  and  Gratifications  Theory  

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According  to  the  uses-­‐and-­‐gratifications  theory  (Katz,  Blumler,  &  Gurevitch,  

1974),  media  use  revolves  around  achievement  of  specific  goals.  Thus,  media  

consumers  have  a  motive  and  preference  which  drives  the  consumption  of  a  

particular  source,  and  that  motive  typically  revolves  around  the  benefits  received  

upon  utilizing  that  particular  media  outlet  over  another.  While  media  producers  use  

highly  trafficked  avenues  as  a  means  of  getting  messages  across  to  interested  

parties,  media  consumers  “frame  what  they  stand  to  gain  from  consuming  a  media  

channel,  or  what  they  will  lose  by  avoiding  […]  that  media  source”  (Browning  &  

Sanderson,  2012).  Essentially,  producers  cast  a  wide  net  in  order  to  gain  an  

audience,  and  consumers  sift  through  these  various  sources  in  order  to  find  the  

outlets  which  are  most  personally  beneficial.    

Gratifications  Rooted  in  Accessibility  

Though  the  Uses  and  Gratifications  theory  originated  from  traditional  media  

use  (i.e.  news  papers,  radio  shows,  and  television  networks),  Ruggiero  applied  this  

theory  to  the  realm  of  social  media,  claiming  that  the  Internet  extends  full  time  

interactivity,  demassification,  and  asynchroniety  (Ruggiero,  2000).  Therefore,  social  

media  users  have  complete  discretion  over  what  is  addressed,  no  time  constraints  

as  to  when  UGC  is  presented,  and  even  have  a  direct  method  of  contacting  these  

content  producers.  These  freedoms  of  content,  time,  and  interactivity  play  a  majorly  

persuasive  role  for  prospective  student  athletes.  For  example,  a  prospective  athlete  

in  California  can  explain  why  he  committed  to  a  particular  school  via  Twitter,  in  one  

hundred  and  forty  characters  or  less,  and  athletes  all  along  the  east  coast  can  

instantly  receive  and  respond  to  that  opinionated  information.    

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  This  increased  access  has  both  benefits  and  implications  for  student-­‐athletes  

on  many  levels.  Not  only  can  consumed  information  persuade  these  athletes  to  

attend  a  particular  college,  which  could  be  seen  as  beneficial  depending  on  the  

institution,  the  accessibility  factor  subjects  athletes  to  critical  tweets  and  direct  

messages  from  angry  fans  or  nosey  reporters.  These  critical  tweets  are  also  

persuasive  for  a  media  consumer’s  behavior  due  to  the  invoked  emotional  effects  

and  responses.  Critical  stimuli  often  warrant  a  self-­‐defense  rebuttal,  which  places  

the  athlete  under  strict  scrutiny  from  coaches,  school  administrators,  and  the  NCAA  

(Browning  &  Sanderson,  2012).  Along  with  the  negative  effects  resulting  from  

player-­‐observer  interaction  is  the  negative  implications  of  player  interaction  across  

teams.  On  December  10,  2011  the  University  of  Cincinnati  and  Xavier  University  

basketball  teams  ended  a  regular  season  matchup  in  fisticuffs,  stemming  from  

negative  twitter  interactions  between  players  on  each  team  (DeCourcy,  2011).  

Players  were  suspended  and  teams  fined,  but  this  event  could  have  been  avoided  

altogether  if  it  were  not  for  the  accessibility  created  by  combined  the  use  of  social  

media.    

With  Access  Comes  Power  

  While  these  aforementioned  events  prove  that  the  access  and  interaction  

capabilities  of  social  media  users  can  influence  behavior,  a  deeper  look  into  media  

and  social  influence  in  general  will  prove  that  exposure  to  content  also  plays  a  

pivotal  role  in  adolescent  behavior.  A  2010  study  by  an  online  edition  of  Pediatrics  

shows  an  increase  in  prevalence  of  product  placement  in  200  of  the  nation’s  top  

grossing  movies  since  1995  (Sutherland,  Mackenzie,  Purvis,  &  Dalton,  2010).  These  

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products,  which  advertising  agencies  subtly  placed  throughout  various  scenes  of  the  

studied  movies,  were  largely  from  “energy  dense,  nutrient  poor  foods  or  product  

lines”  (Sutherland,  et.  Al,  2010).  This  trend  of  product  placement  is  also  directly  

correlated  with  an  increasing  child  obesity  rate  in  America,  and  though  there  cannot  

be  a  specific  scientific  link  of  these  obesity  increases  to  an  increase  in  media  

advertising,  many  connections  can  be  made.  For  example,  increased  advertising  

means  increased  knowledge  of  a  particular  product;  and  sometimes,  knowledge  of  

existence  is  all  one  needs  when  it  comes  to  making  a  choice  between  a  known  and  

potentially  unhealthy  product,  versus  an  unknown  and  potentially  healthier  food  

choice.  

  Further,  a  Loyola  University  study  of  high  school  social  networks  further  

exemplifies  the  power  of  social  influence.  This  study  “was  designed  to  determine  the  

reason  why  obesity  and  related  behaviors  cluster  in  social  networks”  (Ritter,  2012).  

Essentially,  researches  wanted  to  determine  the  power  of  social  influence,  or  the  

ability  of  peers  to  influence  each  other.  This  one-­‐year  study  ultimately  found  that  

students,  who  originally  had  Body  Mass  Indexes  (BMI)  higher  than  those  of  their  

friends,  had  a  40  percent  chance  of  lowering  their  BMI’s  over  the  course  of  the  year  

(Ritter,  2012).  Further,  students  who  were  socially  connected  with  peers  of  a  higher  

BMI  than  themselves  had  a  56  percent  chance  of  increasing  their  own  BMI  closer  to  

the  levels  of  their  friends  over  that  same  year  (Ritter,  2012).  Therefore,  social  

connection  and  peer  behavior  is  also  extremely  influential  in  adolescent  behavior  

and  choices.    

Narcissistic  Productions  

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A  combination  of  these  previously  examined  influences  are  what  my  study  

credits  with  persuasive  power  amongst  student  athletes.  Though  this  study  revolves  

around  a  network  analysis  rather  than  content  analysis,  there  are  general  

assumptions  that  can  accurately  be  made  when  considering  the  UGC  that  is  

consumed  by  recruits.  Previous  survey  analysis  of  a  large  population  of  college  

students  done  by  McKinney,  Kelly,  and  Duran  (2010)  showed  that  college  aged  

students  frequently  displayed  higher  levels  of  narcissism  through  self-­‐centered  UGC  

postings.  Further,  social  identities  of  prospective  student-­‐athletes  are  often  

“grounded  in  attachments  to  teams,”  thus  meaning  that  both  online  and  personal  

identities  of  these  athletes  most  commonly  revolve  around  being  self-­‐proclaimed  

members  of  a  specific  team  and  having  an  allegiance  to  that  particular  team  

(Browning  &  Sanderson,  pg.  506,  2012).  Therefore,  one  can  assume  that  the  UGC  

being  posted  by  many  recruits  involves  some  sort  of  self  and  team  marketing  or  

promotion.  Furthermore,  this  type  of  athletic  propaganda  lays  the  foundation  for  the    

creation  of  social  norms  and  widespread  acceptance  within  media  networks;  thus  

making  consumption  of  this  information  an  influential  factor  in  media  consumers’  

behavior.  These  assumptions  of  content,  paired  with  the  previously  exemplified  

influential  power  of  peers  and  media  within  a  social  network,  allow  a  strong  

theoretical  backing  for  the  results  of  my  network  analysis  which  will  show  the  

persuasive  power  of  central  communication  nodes  within  a  recruiting  class.    

Application  to  University  Athletics  

  This  study  will  be  critical  not  only  for  coaches  and  recruiters,  but  for  the  

social  media  using  student-­‐athlete  as  well.  While  coaches  stand  to  benefit  from  the  

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network  analysis  knowledge,  the  student  athlete  benefits  from  knowing  the  

influence  that  he  or  she  has  via  UGC  on  social  media  sites.  For  example,  the  poor  use  

of  social  media  plays  a  major  role  in  life  beyond  college  athletics  as  well.  While  many  

businesses  differ  from  major  league  athletic  clubs  in  the  necessary  qualifications  of  

who  they  hire  to  be  the  face  of  their  organizations,  there  is  little  difference  in  the  

type  of  expected  character  that  each  representative  should  have.  The  2011  example  

of  Zach  Houchins  proves  this  dynamic.  While  he  was  drafted  in  his  junior  year  of  

college  in  2011  to  the  Washington  Nationals,  he  ultimately  returned  to  school  after  

the  resurfacing  of  racist  tweets  from  an  earlier  time  (Kilgore,  2011).  Thus,  student-­‐

athletes  are  unknowingly,  and  more  commonly  than  not,  harming  their  future  selves  

by  social  media  content  and  interaction.  However,  while  accessibility  and  content  

are  more  commonly  associated  with  negative  implications,  not  all  social  media  

interaction  generates  negative  responses.  

  Recruiting  high-­‐quality  prospective  intercollegiate  athletes  is  a  multi-­‐million  

dollar  business  that  plays  a  major  role  in  home  game  attendance,  coaches’  job  

security,  and  overall  win-­‐loss  record.  While  this  business  of  recruiting  is  regulated  

with  intentions  of  creating  a  level  playing  field  amongst  all  participating  schools,  

coaches  and  programs  will  do  whatever  it  takes  to  get  a  highly  sought  recruit  to  

commit  to  their  institution.  While  the  infiltration  of  a  prospective  athletes’  friend  

base  and  home  life  is  already  a  commonly  used  method  by  coaches  across  the  

country,  the  infiltration  of  these  athletes’  social  media  accounts  is  becoming  a  more  

widely  used  practice.  However,  this  method  is  not  executed  through  direct  

interaction,  but  instead  by  indirect  persuasion.  As  one  big-­‐time  recruit  commits  to  

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an  institution,  coaches  often  ask  for  his  help  in  bringing  in  other  prospects  from  his  

school,  area,  or  skill  set.  While  a  form  of  this  technique  was  most  notably  used  by  

coach  Jimmy  Johnson  of  the  University  of  Miami  in  the  late  1980’s,  the  nature  of  this  

method  has  shifted  (Corben,  2010).  Now,  instead  of  players  calling  each  other  in  

order  to  connect,  they  can  simply  send  out  a  recruiting  pitch  through  a  short  Tweet  

or  message.  Further,  in  accordance  with  the  uses-­‐and-­‐gratifications  theory,  

committed  recruits  feel  that  they  have  taken  part  in  the  class  building  process,  thus  

developing  ownership,  power,  and  pride  in  their  future  schools  (Hermans,  Kempen,  

&  Van  Loon,  2004).  Hermans  (2004)  “observed  that  the  expansion  of  digital  media  

escalates  dialogical  possibilities.  That  is,  individuals  become  multi-­‐voiced  […]  

offering  one  exposure  to  a  wide  variety  of  people,  whose  voices  become  part  of  ones  

own  private  culture”  (Browning  &  Sanderson,  2012).  Therefore,  this  social  media  

interaction  is  a  process,  which  not  only  allows  for  accessibility,  but  also  eases  

communication  through  an  adaptation  of  cultures.    

Summary  

While  the  uses  and  gratifications  theory  provides  justification  for  recruits  to  

use  social  media  in  order  persuade  and  inform  their  prospective  peers,  this  tool  of  

social  media  also  provides  many  additional  benefits  for  coaches  and  recruiting  

coordinators  alike.  For  example,  if  a  Division  1  football  team  offers  verbal  

scholarships  to  seventy  prospective  student  athletes,  coaches  will  need  to  take  time  

out  of  their  busy  in-­‐season  schedules  to  call,  visit,  and  send  letters  to  all  seventy  of  

these  offered  recruits.  However,  if  this  large  pool  of  scholarship  candidates  has  a  

few  people  who  are  leaders  or  friends  with  the  remaining  members  of  the  desired  

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class,  then  the  coaches  would  be  able  to  focus  more  of  their  efforts  on  these  few  

prospects,  and  thus  devoting  more  time  to  their  current  athletic  season,  with  hopes  

that  this  recruit  will  do  his  or  her  own  part  in  building  the  rest  of  the  class.  

This  aforementioned  process  would  identify  “which  student-­‐athletes  are  at  

the  ‘hub’  of  a  team’s  Twitter  activity  and  how  their  Twitter  content  shapes  that  of  

their  teammates”  (Browning  &  Sanderson,  pg.  518,  2012).    Gathering  this  

information  will  provide  a  clear  degree  of  importance  that  intercollegiate  athletic  

departments  and  teams  will  need  to  place  on  the  marketing  and  communicating  

processes  of  social  media  (Clavio,  pg.  322,  2011).  Thus,  through  a  conducted  

network  analysis,  athletic  departments  would  be  able  to  see  which  recruits  are  

social  “lynchpins,”  ultimately  enabling  the  devotion  of  less  administrative  

manpower  towards  the  building  of  a  recruiting  class,  but  rather  allowing  the  

prospects  to  recruit  themselves.  

The  concept  of  self-­‐recruitment,  or  peers  recruiting  peers,  can  be  attributed  

to  the  Theory  of  Planned  Behavior  (Ajzen,  1991).  This  theory  basically  claims  that  

behavior  can  be  predicted  based  on  a  person’s  ability  and  intention.  These  

intentions,  however,  are  subject  to  outside  influence.  Further,  “elements  of  behavior  

are  influenced  by  subjective  norms  within  a  particular  culture.  If  behavior  is  

considered  normal  within  a  group,  it  is  generally  more  likely  that  an  individual  will  

engage  in  it”  (Clavio,  pg.  313,  2011).  Therefore,  a  heavily  followed  recruit  can  create  

a  social  norm  through  his  or  her  UGC  which  is  then  consumed  by  other  athletes  of  

the  same  group,  and  thus  potentially  playing  a  persuasive  role  in  the  behavior  of  the  

media  consumers.  

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Ultimately,  this  is  a  study  of  player  interaction  and  connectivity.  As  this  

review  previously  mentioned  the  negative  results  stemming  from  increased  

accessibility,  there  are,  in  fact,  positive  benefits  to  the  new  accessibility  that  social  

media  use  has  created.  Because  this  new  style  of  recruiting  has  yet  to  be  explored,  I  

wish  to  fill  this  void,  using  the  sample  population  of  University  of  Virginia’s  2014  

Football  recruiting  class,  by  seeking  an  answer  to  the  following  question:  

 RQ1:  Who  are  the  central  nodes  of  communication  within  the  University  of  Virginia’s  2014  recruiting  class?    Methods:    About  The  Study  

  This  study  uses  easily  compiled,  accessible,  and  recordable  data  found  on  

subjects’  online  twitter  accounts.  This  study’s  descriptive  analysis  design  allows  for  

the  empirical  findings  of  a  social  network  analysis  (SNA)  to  be  compared  with  the  

strengths  of  the  relationships  within  these  findings.  The  SNA  conducted  here  will  

examine  the  degree  centrality  of  peers  within  the  same  high  school  recruiting  class.  

The  information  found  will  provide  insight  as  to  which  nodes,  or  recruits,  are  the  

central  nodes  of  communication  within  this  aforementioned  recruiting  class.  

Data  Source  

  Using  a  relatively  large  sample  size  of  eighty-­‐seven  verbally  offered  

University  of  Virginia  football  recruits  from  the  high  school  class  of  2015,  we  will  be  

able  to  asses  the  network  of  communication  amongst  the  common  peers.  Using  

information  found  on  public  but  personal  social  media  web  pages,  a  comprehensive  

database  of  “followers”  will  be  compiled  for  each  individual  subject  or  node.  

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Because  most  of  the  subjects  have  a  twitter  page  which  is  open  to  all  Internet  users,  

it  is  both  legal  and  ethical  to  assess  their  account  information  without  consent.  

However,  some  of  the  subjects  have  private  twitter  accounts;  this  means  that  the  

researcher  will  need  to  personally  request  to  follow  these  subjects  from  another  

account  in  order  to  gather  the  needed  information.  This  request  process,  though,  is  

very  routine,  and  can  be  done  so  through  the  simple  click  of  a  button  and  without  

any  direct  contact  between  researchers  and  the  individuals  themselves.  

  The  selection  of  this  sample  revolves  around  the  athletes’  standings  in  their  

recruitment  processes,  specifically  with  UVA.  This  study  is  most  effective  when  the  

recruiting  class  is  at  its  largest  size,  meaning  the  coaching  staff  has  given  out  the  

highest  amounts  of  verbal  offers  that  it  is  going  to  give  for  the  remainder  of,  in  this  

case,  the  2015  class.  Further,  because  the  optimal  time  to  conduct  this  analysis  falls  

between  November  and  January,  a  large  portion  of  the  offered  prospects  will  have  

had  opportunities  to  attend  recruitment  events,  such  as  a  UVA  home  football  game,  

which  increases  the  probability  that  nodes  will  have  had  in-­‐person  contact  with  

each  other,  thus  leading  to  a  virtual  connection  as  well.  Essentially,  it  is  very  

common  for  recruits  to  exchange  social  media  handles  with  their  peers  at  these  

recruiting  events,  and  the  timing  of  this  study  falls  within  the  realm  of  possibility  for  

these  exchanges  to  have  occurred.  

Instrumentation  

The  original  pool  of  eighty-­‐seven  candidates  was  narrowed  to  57  accessible  

candidates  after  research  was  conducted  on  all  of  the  original  sample  size.  This  

decrease  in  sample  size  was  expected,  considering  that  not  everyone  uses  social  

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media,  and  not  everyone  who  does  use  social  media  has  public  profiles  that  have  

their  actual  legal  names  attached  to  their  accounts.  Essentially,  smart  users  

sometimes  make  it  tough  to  find  their  Twitter  handles,  and  others  are  not  using  

Twitter  at  all.  Once  the  names  of  followers  are  collected  and  logged  into  an  Excel  

spreadsheet,  it  will  be  uploaded  into  MeerKat,  a  social  network  analysis  software  

(Borgatti,  2002).  MeerKat  makes  the  inserted  spreadsheets  symmetric,  and  preps  

the  data  for  its  input  into  the  NetDraw  program.  NetDraw  then  creates  a  visual  of  

the  imputed  data.  This  data  will  be  measured  by  degree  centrality,  which  indicates  

the  strength  of  individual  connections  as  well  as  the  strength  of  the  overall  network  

(Wasserman  &  Fost,  1994).  

In  addition,  network  strength  will  also  be  determined  by  the  measure  of  

network  density;  a  comparison  of  actual  connects  versus  all  possible  connections  

within  a  network  (Wasserman  &  Fost,  1994).  Density  can  be  calculated  by  the  

formula:  Density=[T/n(n-­‐1)]/2;  where  “T”  pertains  to  the  number  of  ties  within  a  

social  network  and  “n”  refers  to  the  number  of  nodes  (Wasserman  &  Fost,  1994).  

Further,  the  resulting  images  and  data  from  MeerKat  and  NetDraw  will  be  analyzed  

to  find  emerging  patterns  within  the  results,  which  can  be  used  to  illustrate  the  

utility  of  the  yielded  information.  

Network  Visualization  

  The  visual  mappings  yielded  by  NetDraw  are  results  of  the  information  

which  has  been  processed  through  Excel  and  Meerkat.  The  maps  produced  by  

NetDraw  will  appear  as  a  web  of  circles  and  bowed  lines.  Each  circle  represents  a  

UVA  class  of  2016  recruit  and  each  line  represents  a  Twitter  connection,  or  follow.  

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Two  lines  between  two  nodes  will  create  a  oval-­‐like  image,  and  this  image  indicates  

two  mutual  follows  between  respective  nodes.  MeerKat  and  NetDraw  allow  for  the  

user  to  edit  the  color  of  nodes,  thickness  of  lines,  and  overall  visualization  of  the  

network  in  order  to  make  the  image  most  effective  for  recipients.  However,  line  

thickness  will  not  be  necessary  for  this  visualization  because  instead  of  basing  our  

connections  off  of  varying  values  of  in  and  out  degree  connections,  we  value  any  

connection  as  1,  and  any  non-­‐connection  as  0.  Therefore,  the  nodes  with  more  oval-­‐

like  connections  still  have  a  higher  value  in  the  computed  metrics,  but  do  not  show  a  

thicker  line.  This  study  will  find  the  nodes  with  the  highest  degree  of  centrality,  and  

then  asses  whether  or  not  this  measurement  is  the  most  effective  in  determining  

persuasion  power,  and  critical  relationship  with  the  rest  of  the  class.  

  While  viewing  the  entire  network  will  be  important  for  the  coach  or  athletic  

department  receiving  this  information,  the  ability  to  break  down  and  isolate  certain  

people  or  relationships  within  the  whole  is  crucially  important  to  understanding  

network  cohesion.  The  individual  or  smaller  group  of  individuals  being  studied,  in  

this  case,  is  referred  to  as  clusters.  Cluster  networks  are  compiled  of  an  individual  or  

a  group  of  individuals  along  with  all  of  their  connections.  The  groups  or  people  with  

the  highest  quality  and  number  of  connections  are  the  individuals  we  are  searching  

to  label  as  the  main  propeller  of  information  and  connectivity  within  the  recruiting  

class.  The  most  connected,  or  followed  node  within  the  entire  network  will  be  

considered  a  high-­‐value  target  (HVT),  thus  giving  coaches  and  recruiting  

coordinators  players  to  focus  on  during  the  recruitment  process.  These  clusters,  

  16  

however,  will  have  separate  representation  and  value  throughout  the  “Results”  

section  of  this  discourse.  

Definitions  

  A  crucial  part  in  being  able  to  understand  the  visuals  which  are  

manufactured  by  this  study,  is  understanding  the  terminology  which  is  used  to  

define  relationships  amongst  clusters,  nodes,  or  entire  networks.  The  Indegree  

measurement  refers  specifically  to  the  amount  of  connections  that  a  node  has  within  

a  cluster  or  network,  and  for  all  intensive  purposes  of  this  study  will  be  simply  

referred  to  as  “centrality.”  While  Indegree  measures  connection,  closeness  measures  

path  distance,  and  thus,  overall  effectiveness  of  passing  information.  A  node  with  the  

highest  closeness  percentage  means  that  the  path  distance  between  that  node  and  

any  other  node  in  the  network  or  cluster  is  shorter  or  more  direct  than  another  

other  node  has  with  the  remainder  of  the  group.  The  other  measurement  used  in  

this  study,  and  that  has  to  do  with  path  distance,  is  betweeness.  This  measurement  

is  used  to  describe  which  node  has  the  shortest  paths  and  most  direct  relationships  

with  the  rest  of  his  direct  connections.  The  final  measurement  used  is  based  on  a  

compilation  of  path  distance  and  indirect  connections,  and  is  referred  to  as  hub.  The  

hub  percentages  show  how  likely  and  effectively  this  individual  can  reach  the  rest  of  

the  cluster  or  network.  

Limitations  To  the  Study  

This  study  suffers  from  a  limited  sample  size,  which  may  not  be  indicative  of  

the  larger  process  of  college  recruiting.  This  selection  bias,  however,  was  

unavoidable  due  to  a  lack  of  public  information  on  other  schools’  recruiting  classes  

  17  

and  recruit  contact  information.  Having  a  direct  link  to  the  University  of  Virginia  

Football  program,  I  was  easily  able  to  obtain  my  sample  population,  whereas  I  

would  have  needed  to  disassociate  with  UVA’s  program,  and  jump  through  various  

other  hoops  of  consent  to  obtain  this  information  from  other  NCAA  institutions.  

Further,  as  previously  mentioned,  this  study  is  not  a  content  analysis.  While  

subsequent  studies  have  created  a  strong  theoretical  basis  leading  a  researcher  to  

accurately  predict,  both,  the  type  of  UGC  stimuli  posted  by  student  athletes,  and  the  

power  that  this  stimuli  has  over  there  peers,  there  are  always  outliers  to  the  mold.  

This  historical  threat  to  the  internal  validity  of  the  study  is  essentially  unavoidable  

as  I  am  working  alone  on  this  analysis,  and  there  is  not  enough  time  to  do  both  a  

content  and  network  analysis  of  the  sample  population.  However,  outliers  to  these  

previously  mentioned  theories  which  assume  content  and  persuasion  would  be  an  

abnormality  that  is  another  study  in  and  of  itself.  

 

Results       While  the  main  goal  of  this  study  was  to  find  the  central  nodes  within  the  

2016  recruiting  class,  the  Social  Network  Analysis  conducted  yielded  much  more  

useful  information  about  the  sample  size  than  just  centrality.    The  information  found  

on  the  entire  2016  class  yielded  relatively  low  correlations,  producing  a  network  

with  low  network  density,  a  high  average  path  of  separation,  and  an  information  

Hub  with  low  access  to  the  rest  of  the  group.  However,  this  information  was  

restricted  due  to  the  large  number  of  generally  unconnected  nodes  within  the  

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recruiting  class.  As  you  can  see  in  FIGURE  1,  there  are  19  nodes  that  are  completely  

disconnected  from  the  group.    

Figure  1:  

   

Further,  there  are  four  nodes  having  only  one  other  connection  within  the  class.  

This  separation  from  the  main  group  largely  decreases  the  statistical  significance  of  

the  findings.  However,  this  data  is  not  rendered  useless.  It  is  important  to  know  

which  nodes  cannot  be  reached  by  this  means  of  social  media,  and  which  nodes  can.  

  19  

Therefore,  UVA  coaches  know  that  the  way  to  convince,  node  18,  for  example,  to  

come  to  UVA  will  not  be  by  means  of  Twitter.    

   When  taking  a  further  look  at  FIGURE  1  you  can  see  that  the  recruiting  class  

is  characterized  by  two  main  clusters  which  are  connected  only  through  a  few  

nodes.  Therefore,  these  findings  are  further  broken  down  into  main  body  results  

(which  is  the  findings  of  the  two  main  connected  clusters),  Cluster  A  findings,  and  

Cluster  B  findings.  When  limiting  the  research  to  eliminate  the  nodes  that  cannot  be  

reached,  the  statistical  data  increases  significantly.  Just  by  looking  at  FIGURE  2  you  

can  see  that  there  are  three  nodes  showing  “flower-­‐like”  relationships,  or  oval-­‐like  

connections,  to  surrounding  nodes.  Figure  2:

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These  flowers  indicate  mutual  follows,  and  thus,  a  high  degree  of  centrality.  

While  node  39,  12,  and  53  all  have  the  highest  degree  of  centrality  with  numbers  

ranging  above  25%  of  the  total  network  (See  Chart  1),  the  connections  between  

nodes  15  and  12,  and  53  and  29  represent  the  only  connections  which  link  cluster  A  

and  B  together.  Therefore,  node  53,  or  @Brandonw_7,  would  be  the  most  crucial  

node  keeping  the  entire  accessible  network  alive  and  effective.  So,  while  53  may  be  

limited  as  to  who  it  can  directly  contact  and  influence,  it  has  the  most  indirect  

  21  

relationships,  and  serves  as  the  information  hub  connecting  the  two  main  body  

clusters.    

Further,  separating  the  two  clusters  into  their  own  individual  entities  will  

help  us  to  take  a  better  look  at  the  network’s  components.  Figure  3  represents  

Cluster  A’s  social  network  visual.    

Figure  3:  

         

  22  

   Cluster  A’s  node  53  has  a  very  high  degree  of  centrality,  but  while  node  53  is  

more  centralized,  it  is  virtually  equal  with  node  15  in  its  effectiveness  to  reach  the  

rest  of  cluster  A  (Closeness).  In  fact,  node  15  actually  has  the  more  concise  routes  to  

its  direct  connections  (Betweeness).    

   

Additionally,  it  is  also  important  to  note  the  connections  between  individual  

nodes.  For  example,  lets  say  a  coach  was  only  trying  to  recruit  Node  3.  This  coach  

could  then  use  the  SNA  in  order  to  find  this  node’s  most  direct  connection  (which  in  

this  case  is  node  50).  Deeper  analysis  of  the  two  main  groupings  would  also  show  

that  Cluster  A  will  have  the  most  efficient  passage  of  information  throughout.  This  is  

proven  by  its  shortest  average  path  distance  of  1.6,  while  Cluster  B  and  the  Main  

Body  both  have  average  path  distances  above  2.    

0.00%  

10.00%  

20.00%  

30.00%  

40.00%  

50.00%  

60.00%  

70.00%  

1   3   8   9   15   27   28   41   50   53  

Percentage  

Node  

Cluster  A  

Hub  

Betweenness  

Closeness  

InDegree  

  23  

  Similarly,  Cluster  B  analysis  shows  higher  degrees  of  connections  amongst  

the  recruits,  with  node  39  being  the  most  crucial  actor.  Figure  4  represents  Cluster  

B’s  analysis  

 Figure  4:  

   Not  only  is  this  node  the  most  centralized,  but  it  also  holds  leading  percentages  in  all  

other  categories  besides  closeness,  in  which  it  marginally  loses  out  to  node  54.  It  is  

also  important  to  note  that  Cluster  B  contains  node  39,  which  has  the  highest  degree  

centrality  of  the  entire  network  of  recruits.  Therefore,  while  Cluster  A  may  be  more  

efficient  in  reaching  its  components  through  the  shortest  average  path  distance,  

  24  

Cluster  B  contains  a  crucial  node  that  has  much  farther  reach  than  any  node  in  

Cluster  A.  So  while  the  numbers  in  Cluster  B’s  chart  may  be  lower  than  those  in  its  

predecessor’s,  Cluster  B  still  plays  a  very  crucial  role  in  the  overall  social  network  

analysis

 

     

 

0.00%  

10.00%  

20.00%  

30.00%  

40.00%  

50.00%  

60.00%  

2   4   7   12   13   14   24   25   26   29   31   33   36   37   39   40   42   43   46   47   54  

Percentage  

Node  

Cluster  B  

Hub  

Betweeness  

Closeness  

Indegree  

  25  

Conclusion       Cluster  B’s  node  39,  more  commonly  known  to  his  followers  as  Tim  Settle  of  

Manassas,  Virginia,  is  statistically  the  most  crucial  node  in  the  connected  body  of  the  

network.  Not  only  does  Tim  have  the  highest  degree  centrality,  but  also  he  has  the  

greatest  ability  to  pass  information.  But  why?  As  a  four-­‐star  recruit  according  to  

Rivals.com,  Tim  is  heavily  recruited  by  many  of  the  top  schools  in  the  nation.  While  

he  may  not  have  the  most  Twitter  followers  when  compared  with  the  rest  of  his  

recruiting  class,  he  has  been  on  a  considerably  high  number  of  official  and  unofficial  

visits  throughout  his  junior  year  of  high  school.    

  These  visits  put  Tim  in  direct  contact  with  other  high  school  recruits,  where  

clearly  they  make  a  personal  connection,  which  apparently  later  leads  to  a  virtual  

connection.  Because  Tim  is  such  a  high  caliber  recruit  (as  proven  by  his  four-­‐star  

rating  rather  than  the  more  plentiful  two  or  three-­‐star  recruits  within  UVA’s  2016  

class),  he  also  carries  an  “awe”  factor,  which  could  also  play  a  role  in  his  high  degree  

of  centrality.  I  believe  that  the  most  interesting  part  of  Tim’s  role  as  the  most  crucial  

actor  within  the  network,  is  that  UVA  has  changed  their  recruiting  tactics  to  focus  

more  largely  on  the  Virginia  Beach  area  rather  than  the  Northern  Virginia  area  

where  Tim  is  from.    Typically,  it  is  obvious  when  people  know  each  other  through  

localities  and  hometown  identification.  However,  because  Tim’s  connections  range  

much  farther  than  Northern  Virginia,  this  means  that  Tim  is,  in  fact,  a  bit  of  a  star  

within  his  own  class,  and  is  not  obtaining  these  crucial  connections  simply  due  to  

hometown  linkages.  

  26  

  All  of  this  information  and  analysis  is  aimed  that  the  efficient  targeting  of  

specific  athletes  within  a  class.  Therefore,  if  UVA  wanted  to  obtain  a  specific  recruit,  

they  could  effectively  offer  a  centralized  node,  such  as  Tim  Settle,  and  put  large  

amounts  of  recruiting  efforts  into  getting  him  to  commit  first,  rather  than  the  

multiple  athletes  they  are  actually  trying  to  reach.  Then,  the  coaches  could  rely  on  

Tim  to  be  effectively  pumping  propaganda  to  other  potential  recruits  through  his  

Twitter  account.  The  result  would  be  streamlined  information  to  a  wide  audience,  

and  coaches  spending  less  time  actually  executing  traditional  boots  on  the  ground  

recruiting  tactics.  So,  instead  of  making  phone  calls  and  writing  letters  to  make  sure  

a  desired  prospect  is  still  thinking  about  a  particular  institution,  the  coaches  and  

recruiting  coordinators  can  rely  on  that  prospect’s  peer  to  execute  that  job.  Further,  

this  passage  of  information  from  a  peer,  as  stated  in  the  review  of  literature,  will  

also  serve  as  a  persuasive  factor  due  to  the  position  from  which  the  information  is  

flowing.  

  While  taking  advantage  of  someone’s  social  network  is  a  stealthy  way  to  

incept  prospective  athletes  to  think  about  a  particular  institution,  it  may  not  always  

yield  positive  results.  Just  as  easily  as  this  tactic  may  yield  the  passage  of  positive  

information  along  social  network  wavelengths,  the  potential  for  negative  UGC  is  

possible  as  well.  If  a  coach  targets  a  particular  student  athlete,  and  somewhere  along  

the  way  foils  the  relationship  between  the  university  and  the  prospect,  then  an  

inverse  effect  will  undoubtedly  take  place.  The  University  of  Virginia  is  a  great  

example  of  a  school  with  high  potential  for  these  negative  consequences.  Because  

UVA  has  managed  to  string  together  a  fairly  large  amount  of  losing  seasons,  there  

  27  

has  been  a  high  turnover  of  assistant  coaches  in  the  past  5  years.  If  an  assistant  

coach  has  forged  a  relationship  with  a  particular  central  node,  and  is  later  relieved  

of  his  duties  due  to  lacking  performance,  then  it  could  lead  to  the  central  node’s  

public  disapproval  of  administrative  action.  This  disapproval  would  then,  in  fact,  

warrant  more  work  on  behalf  of  the  replacement  staff  in  order  to  mend  not  only  the  

relationship  with  the  central  node,  but  also  the  relationships  of  the  ones  with  whom  

that  node  is  connected.      

That  being  said,  proper  analysis  of  the  social  connections  may  adequately  

prevent  the  spread  of  negative  information.  If  coaches  know  which  recruit  is  the  

central  node,  then  they  will  know  whose  offer  not  to  pull  when  National  Signing  Day  

comes  around  and  schools  need  to  trim  down  the  amount  of  verbal  offers  that  have  

already  been  extended.    For  example,  if  coaches  know  that  the  central  node  of  a  

recruiting  class  is  a  two-­‐star  athlete,  then  coaches  may  keep  their  scholarship  offer  

extended  in  order  to  limit  the  negative  press,  regardless  of  the  fact  that  the  recruit  is  

essentially  interchangeable  in  the  larger  scheme.  Without  this  knowledge,  coaches  

would  more  than  likely  pull  that  recruit’s  offer,  and  potentially  suffer  the  

consequences  in  terms  of  a  social  media  berating.    

While  my  sample  size  ended  up  to  be  relatively  small  and  only  applicable  to  

one  school’s  recruits,  there  is  nothing  stopping  this  from  being  a  national  practice.  

This  larger  analysis  would  yield  information  about  the  entire  nation’s  recruits  and  

their  connections,  potentially  leveling  the  recruiting  playing  field  for  mid-­‐major  and  

lesser  conference  teams.  Instead  of  sending  letters  to  top  recruits  which  would  

undoubtedly  never  be  opened,  they  could  access  top  recruits  in  140  characters  or  

  28  

less  via  a  trusted  friend’s  own  medium.  Not  only  could  this  ease  access,  but  this  

could  also  save  some  athletic  departments  across  the  country  a  lot  of  money  in  the  

long  run.  Coaches  will  no  longer  need  to  make  frequent  trips  to  high  schools  around  

the  nation  just  to  talk  to  a  recruit  for  twenty  minutes.  Instead,  the  pitch  will  be  done,  

and  coaches  can  make  less  frequent  and  therefore  more  meaningful  trips  to  see  

these  prospects  when  it  is  closer  to  signing  day  and  recruits  need  some  last  minute  

persuasion.    

This  technique  would  not  only  be  cost  cutting  for  athletic  departments,  but  

for  general  higher  education  as  well.  Administrators  are  constantly  and  actively  

searching  for  ways  to  increase  the  national  prestige  of  their  particular  university.  

While  schools  often  gain  prestige  as  a  result  athletic  achievements  placing  them  in  a  

national  media  spotlight,  not  all  sports  teams  can  be  successful,  and  some  schools  do  

not  reap  the  benefit  from  NCAA  competition.  With  a  social  network  analysis  of  a  

potential  admits  to  a  college  or  university,  administrators  can  better  diversify  their  

student  body  over  a  period  of  time  by  quickly,  cheaply,  and  effectively  getting  their  

institution’s  name  out  across  the  world.  For  example,  Sweet  Briar  College  is  a  small  

school  with  very  little  national  and  international  recognition.  However,  if  Sweet  

Briar  decided  to  use  a  social  network  analysis  to  determine  the  reach  and  

relationships  that  potential  students  have  via  UGC,  then  Sweet  Briar  would  

essentially  be  able  to  tailor  their  incoming  class  without  having  to  settle  on  

admitting  an  under  qualified  student  simply  because  of  the  region  he  or  she  lives.  

Instead,  Sweet  Briar  would  be  able  to  find  a  better,  more  qualified  student  with  

  29  

social  ties  to  the  area  they  are  trying  to  reach,  and  see  an  influx  of  applicants  from  

that  area  in  the  coming  years.    

Though  there  are  many  great  implications  for  recruiting  both  for  athletics  

and  general  higher  education,  there  are  some  unwritten  implications  for  

prospective  students  and  student  athletes  across  the  nation.  These  prospects  will  be  

under  a  much  stronger  microscope  from  higher  education  administrators,  and  the  

implications  could  be  great.  Taking  a  closer  look  at  high  school  kids’  Twitter  

postings  could  raise  a  school  or  athletic  department’s  awareness  of  potential  

character  issues,  which  would  not  only  make  administrators  cautious,  but  could  

eventually  cost  the  adolescent  a  chance  at  post-­‐secondary  education.  Also,  if  the  

prospects  figure  out  what  the  universities  are  doing,  then  they  are  more  likely  to  

either  block  their  accounts  from  the  public,  or  even  go  against  their  normal  social  

media  tendencies.    

Just  as  schools  are  limited  in  who  can  know  about  their  recruitment  

processes,  this  research  is  limited  as  well.  My  sample  size  came  from  only  one  

school,  and  the  research  took  place  only  months  after  the  University  of  Virginia  

football  team  went  two  and  ten  in  regular  season  matchups.  Furthermore,  and  as  

previously  stated  in  multiple  accounts,  this  study  was  by  no  means  a  content  

analysis.  There  is  a  chance  that  Tim  Settle  was  not  saying  anything  good  about  UVA  

regardless  of  his  commitment,  and  regardless  of  the  studies  on  narcissism  which  

would  indicate  otherwise.  If  this  is  the  case,  then  the  theories  have  failed  and  the  

practice  may  be  rendered  useless.  I  believe,  however,  that  these  limitations  leave  

room  for  further  study.  Does  college  commitment  yield  propaganda-­‐ridden  tweets?  

  30  

Does  social  media  centrality  actually  play  a  factor  in  persuasion?  All  of  these  

questions  fall  outside  of  the  constraints  of  this  study  and  should  be  further  

investigated.    

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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Works  Cited  

Browning, B., & Sanderson, J. (2012). The positives and negatives of twitter: Exploring how student athletes use twitter and respond to critical tweets. International Journal of Sport Communication, 5, 503-521. Retrieved from Chronicle.com/blogs/players/files/2012/12/athletes_twitter.pdf Corben, Billy, dir. "The U." ESPN 30 for 30. ESPN: 12 Dec 2009. Television. Clavio, G. (2008). Why we follow: An examination of social interaction and fan motivations for following archetypes on twitter. International Journal of Sport Communication, 5, 481-502. Retrieved from http://www.academia.edu/3256749/Why_We_Follow_An_Examination_of_Parasocial_Interaction_and_Fan_Motivations_for_Following_Athlete_Archetypes_on_Twitter Clavio, G. (2011). Social media and the college football audience. Journal of Issues in Intercollegia Athletics, 4, 309-325. Retrieved from csri-jiia.org/documents/pucilcations/research_articles/2011/JIIA_2011_4_17_309_325_SMFCA.pdf DeCourcy. (2011, December 10). Cincinatti xavier game ends in bench clearing brawl. Sporting News, Retrieved from http://www.sportingnews.com/ncaa-basketball/feed/2011-12/ucxu-brawl/story/cincinnati-xavier-game-ends-in-benches-clearing-brawl Hambrick, M. (2012). Six degrees of information: Using social network analysis to explore the spread of information within sport social networks. International Journal of Sport Communication, 5, 16-34. Retrieved from http://journals.humankinetics.com/ijsc-back-issues/ijsc-volume-5-issue-1-march/six-degrees-of-information-using-social-network-analysis-to-explore-the-spread-of-information-within-sport-social-networks Kaplan, A., & Haenlein, M. (2010). Users of the world, unite! the challenges and opportunities of social media. Business Horizons, 4(53), 59-68. Kilgore, A. "Zach Houchins says he is not a racist, nervous about future with the Nationals." Washington Post [Washington, DC] 10 Jun 2011, n. pag. Web. 4 Dec. 2013. <http://www.washingtonpost.com/blogs/nationals-journal/post/zach-houchins-says-he-is-not-a-racist-nervous-about-future-with-the-nationals/2011/06/10/AGdkNyOH_blog.html>. Pegorago, A. (2012). Look whos talking- athletes on twitter: A case study. International Journal of Sport Communication, 3(4), 501-514. Retrieved from http://journals.humankinetics.com/ijsc-back-issues/ijsc-volume-3-issue-4-december-/look-whos-talkingathletes-on-twitter-a-case-study

  32  

Ritter, J. "New evidence for link between teens' social network, weight gain." Health Sciences Divisoin [Maywood, Ill] 11 Jul 2012, n. pag. Print. Ruggiero, T. (2000). Uses and gratifications theory in the 21st century. Mass Communication and Society, 3(1), 3-37. Retrieved from https://umdrive.memphis.edu/cbrown14/public/Mass Comm Theory/Week 7 Uses and Gratifications/Ruggiero.pdf Sanderson, J. (20011). To tweet or not to tweet: Exploring division i athletic departments' social media policies. International Journal of Sport Communication, 492-513. Retrieved from http://journals.humankinetics.com/ijsc-back-issues/ijsc-volume-4-issue-4-december/to-tweet-or-not-to-tweet-exploring-division-i-athletic-departmentsrsquo-social-media-policies Sutherland, L., Mackenzie, T., Purvis, L., & Dalton, M. (2010). Obesity, diet, and activity: Research shows that food and beverage produc placements in movies may be potent source of advertising to children. Hood Center for Children and Families, Retrieved from hoodcenter.dartmouth.edu/FoodProductPlacement.html Towers, C. (2011, January 14). [Web log message]. Retrieved from blogs.ajc.com/recruiting/2011/01/14/georgia-bulldogs-offer-scholarship-to-isaiah-crowells-close-friend/    

 

 

 

 

 

 

 

 

 

 

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