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Candidate.ie


 Social media usage by candidates in the
2011 Republic of Ireland General Election




            Dr Ciar叩n Mc Mahon,
          Department of Psychology,
           Dublin Business School.
Background
 2010 US mid-term elections Facebook.com (3 November,
  2010)
    98 House races
       74% of candidates with the most Facebook fans won
    Senate 19 races
       81% of candidates with the most Facebook fans won
 Donegal South-West by-election (24th November, 2010)
    high correlation observed between 1st preference votes
     and Facebook friends (r = 0.889, N = 5, p < 0.05).
CANDIDATE.IE




The Current Study
Data collection
 Follower/fan data collected from 21st to 24th February 2011.
    T-tests comparing data collected at either extreme of time period
     revealed no differences.
 1st preference vote share was collected from the RT News election
 Candidates gender was inferred from their names (!)
 Age was sourced and cross-referenced from various reputable
  online sources, and later politely requested via email.
 Population density data was calculated from constituency area
  measurements gratefully supplied by Richard Cantillion of GAMMA
  Ltd. using D叩il Boundaries provided by Ben Raue,
  www.tallyroom.com.au and population statistics from the Central
  Statistics Office.
Data collection
 Election
   566 candidates
      481 male, 85 female.
   Reliable date of birth sourced for 372
      average 48.22 (range 22  75, st. dev. 11.451).
   125 incumbents stood for election
Data collection
 Twitter
   325 accounts,
      average 467.52 followers (2-11465, st. dev. 1039.81,
       sum 151,945)
      hence 57.4% of all candidates were on Twitter
Data collection
 Facebook
   432 accounts
     average of 730.65 friends/fans/members (1 to 5000,
      st.dev. 875.10, sum 315,640)
     78.8% of all candidates had Facebook accounts of one
      kind or another
Data collection
 Facebook
   432 accounts
      316 Friend accounts, 112 Fanpages, and 3 were groups.
         14 private Facebook accounts were also identified, but as their
          Friend totals were not visible, had to be removed from the final
          analysis.
         When a candidate had both a Friend and Fanpage, the page with
          the highest level of support, as a better indicator of popularity,
          was used (unless the candidate requested otherwise).
         NB no difference between candidates with Friend or Fanpages in
          terms of likelihood of getting elected (U = 17578, N1 = 317, N2 =
          112, p = .850 ) nor more likely to get votes (t = .356, df =
          182.019, p = .722)
ANALYSIS




Presence, Popularity, Votes And Success...
Research Questions
 What factors are related to
   having a presence on social media?
   being popular on social media?
 How does social media presence and
  popularity relate to votes received?
 Ultimately, how does social media relate to
  getting elected....
U have no chance if ur not on
 facebook and twitter. If u can't do
social media then can u do anything
               at all?




                      Male, Labour, Louth
1. Gender
 Presence:
    Facebook
       Female candidates more likely to have an account 2(1, N = 566) =
        4.085, p < 0.5. However weak strength association  = 0.085.
    Twitter
       No difference between genders in having accounts 2(1, N = 566) =
        2.929 , p = 0.087.
 Popularity
    Facebook
       no difference in friends/fans (t = 1.060, df = 430, p = .290)
    Twitter
       no difference in followers (t = -0.367, df = 323, p = 0.714)
2. Age
 Younger candidates expected to be more popular
   Facebook
      no negative correlation observed between a candidates age
       and their number of friends/fans (r = - .059, N = 310, p = >
       .05).
   Twitter
      no negative correlation observed between a candidates age
       and their number of followers (r = .073, N = 254, p = > .05).
   Further analyses, as regards age predicting presence
    on social media, did not reveal models of any
    significance
3. Party affiliation
 Facebook
   Presence
     differences observed across parties in terms of their
      candidates having a Facebook account
        2(10, N = 566) = 81.280, p < 0.001, with moderate strength
         observed  = 0.379
3. Party affiliation
3. Party affiliation
 Facebook
   Popularity
      differences between the parties in terms of their
       candidates popularity on Facebook (F(9,422) = 6.040, p
       < 0.01)
3. Party affiliation
3. Party affiliation
 Twitter
   Presence
      differences between parties and the likelihood of their
       candidates having a Twitter account 2(10, N = 566) =
       86.268, p < 0.001, with moderate strength observed 
       = 0.390
3. Party affiliation
3. Party affiliation
 Twitter
   Popularity
      differences between the parties in terms of their
       candidates popularity on Twitter (F(9,315) = 2.454, p <
       0.05)
Mc Mahon, C. (2011). Social media usage by candidates in the 2011 Irish General Election
4. Constituencies
 Facebook
   Presence
      no differences across constituencies 2(1, N = 42) =
       52.129, p = 0.136.
   Popularity
      no differences across constituencies in terms of
       candidates popularity on Facebook (F(42, 431) = 1.399,
       p = 0.056).
4. Constituencies
 Twitter
   Presence
      no differences across constituencies 2(1, N = 42) =
       43.743 , p = 0.397.
   Popularity
      differences across constituencies in terms of popularity
       on Twitter (F(42, 324) = 1.508, p < 0.05).
Mc Mahon, C. (2011). Social media usage by candidates in the 2011 Irish General Election
5. Urban/rural
 Facebook
   Presence
      no differences between urban and rural candidates
       and their having a Facebook account 2(1, N = 566) =
       0.278, p = .598.
   Popularity
      differences between urban and rural candidates in
       terms of their candidates popularity on Facebook (t =
       2.297, df = 366.277, p < 0.05) with a confidence interval
       of CI95 (27.876, 359.798).
5. Urban/rural
5. Urban/rural
 Twitter
   Presence
      a difference between urban and rural candidates and
       their having a Twitter account 2(1, N = 566) = 3.986, p
       < 0.05, though with only weak strength  = - 0.084.
5. Urban/rural
5. Urban/rural
 Twitter
   Popularity
      statistically differences between the urban and rural
       candidates in terms of their candidates popularity on
       Twitter (t = - 2.267, df = 323, p < 0.05) with a
       confidence interval of CI95 (- 496.006, -35.083).
5. Urban/rural
6. Incumbency
 Facebook
   Presence
      no differences between incumbent and non-sitting with
       regard to their having a Facebook account 2(1, N =
       566) = 3.459, p = .063.
   Popularity
      statistically differences between the incumbent and
       non-sitting candidates in terms of their candidates
       popularity on Facebook (t = 3.532, df = 430, p < 0.001)
       with a confidence interval of CI95 (156.620, 549.525).
6. Incumbency
6. Incumbency
 Twitter
   Presence
      difference between incumbent and non-sitting
       candidates in terms having a Twitter account 2(1, N =
       566) = 13.948, p < 0.001, though with only weak
       strength  = .157.
6. Incumbency
6. Incumbency
 Twitter
   Popularity
      statistically differences between the incumbent and
       non-sitting candidates in terms of their candidates
       popularity on Twitter (t = - 4.126, df = 323, p < 0.001)
       with a confidence interval of CI95 (271.585, 766.660).
6. Incumbency
My advice would be don't set up an
account unless you are going to use
it. I followed several candidates who
rarely posted. And I certainly felt that
many of the candidates had nothing
 to do with their accounts, that it was
 a team member. I would advise that
 if it is a team member, that they are
              upfront about it.




                         Female, Greens, Dublin South
7. Votes
 Correlation
   Facebook
      positive correlation observed between a candidates
       popularity on Facebook and the number of first
       preference votes received (r = .450, N = 432, p < 0.01,
       one-tailed)
   Twitter
      positive correlation observed between a candidates
       popularity on Twitter and the number of first
       preference votes received (r = .164, N = 325, p < 0.01,
       one-tailed)
7. Votes
 First preferences
   Facebook
      difference between the number of first preference votes
       received by candidates who had a Facebook account and
       those who did not F(1, 562) = 6.019, p < .05.
   Twitter
      difference between the number of first preference votes
       received by candidates who had a Twitter account and those
       who did not F(1, 562) = 19.404, p < .001.
   but no interaction effect for having both F(1, 562) =
    1.098, p = .295.
      i.e. an effect for one or the other, but no bonus
7. Votes
Twitter   Facebook   Mean        Std. Dev.     N
Yes       Yes          4885.26      3663.938       306
          No           4241.58      3486.822       19
          Total        4847.63      3651.784       325
No        Yes          3347.78      3032.523       140
          No           1744.45      2952.871       101
          Total        2675.84      3096.398       241
Total     Yes          4402.65      3547.363       446
          No           2139.82      3162.951       120
          Total        3922.90      3588.197       566
Mc Mahon, C. (2011). Social media usage by candidates in the 2011 Irish General Election
7. Votes
 Multiple regression analysis (stepwise method)
    predictor variables:
         number of followers
         number of friends or fans
         incumbency
         population density
         constituency
         urbanity
         affiliation.
    model emerged F(7, 287) = 35.702, p < 0.005 which
     predicted 45.2% of the variance.
 Facebook support, incumbency and party were
  predictors, though the others were not.
7. Votes
                              B          Std. Error     Beta
Number of followers               .029           .160        .009
Number of friends or fans        1.188           .185      .306*
If they had a seat           -1943.192       404.823      -.233*
Constituency                     -.299         13.533       -.001
Urbanity                       117.151       442.337         .016
Population density               -.293           .145       -.120
Affiliation                   -380.579         45.418     -.401*
Pre -Social media there was no way
 Mick Wallace could have amassed
  13,000 first prefernce votes in the
    space of 3 weeks in Wexford.
The lads running his page deserve a
                medal.



                       Male, Independent, Wexford
8. Success
 Facebook
   those with accounts were more likely to get
    elected than those without 2(1, N = 566) =
    14.767, p < 0.0005 . = 0.162.
8. Success
8. Success
 Twitter
   those with accounts were more likely to get
    elected than those without 2(1, N = 566) =
    29.947, p < 0.0005 (though  = 0.230)
8. Success
8. Success
 Incumbents
   no differences among incumbents between those
    who had Facebook accounts as to whether or not
    they won a seat 2(1, N = 125) = .775, p =.397.
   no differences among incumbents between those
    who had Twitter accounts as to whether or not
    they won a seat 2(1, N = 125) = .080, p = .777.
8. Success
 Challengers
   Facebook
      differences between those who had Facebook
       accounts as to whether or not they won a seat 2(1, N =
       441) = 19.339, p = .0005.  = .209
8. Success
8. Success
 Challengers
   Twitter
      differences between those who had Twitter accounts
       as to whether or not they won a seat 2(1, N = 441) =
       24.198, p = .0005.  = .234
8. Success
8. Success
(Continuing to ignore incumbents, no differences)
 Urban
    Facebook
        Challengers who had accounts were not more likely to
         get elected than those who did not 2(1, N = 155) =
         2.845, p = .092.
    Twitter
        Challengers who had accounts were more likely to get
         elected than those who did not 2(1, N = 155) = 8.114, p
         = .005.  = .229.
8. Success
8. Success
(Continuing to ignore incumbents, no differences)
 Rural
    Facebook
        Challengers who had accounts were more likely to get
         elected than those who did not 2(1, N = 286) = 17.577,
         p = .0005.  = .248
    Twitter
        Challengers who had accounts were more likely to get
         elected than those who did not 2(1, N = 286) = 15.972,
         p = .0005.  = .236
8. Success
8. Success
A curious effect...
A curious effect...
A curious effect...
 Facebook
    comparing incumbents/challengers with successful/failed
      candidates
    a significant effect of success on number of friends/fans F(1,
      428) = 34.323, p < .0005
        but no effect of success
 Twitter
    comparing incumbents/challengers with successful/failed
      candidates
        a significant effect of success on number of followers F(1,
          562) = 14.422, p < .05
        but no effect of incumbency
8. Success
 Logistic regression model
    success in election
        predicted using urbanity, population density, party
          affiliation, number of Twitter followers, number of
          Facebook friends/fans, incumbency, age and gender as
          variables
        233 cases examined
        predicted election (omnibus chi-square 146.37, df =
          16, p < 0.0005)
        accounted for between 46.6 and 62.3% of the
          variance, with 81.8% of successful and 84.6% of
          unsuccessful candidates correctly predicted
8. Success
Success


 Interestingly, while the number of Twitter
followers a candidate had does not seem to
     had a impact on their success, each
   Facebook fan or friend increased their
  chance of getting elected by a factor of
                   1.001...

More Related Content

Mc Mahon, C. (2011). Social media usage by candidates in the 2011 Irish General Election

  • 1. Candidate.ie Social media usage by candidates in the 2011 Republic of Ireland General Election Dr Ciar叩n Mc Mahon, Department of Psychology, Dublin Business School.
  • 2. Background 2010 US mid-term elections Facebook.com (3 November, 2010) 98 House races 74% of candidates with the most Facebook fans won Senate 19 races 81% of candidates with the most Facebook fans won Donegal South-West by-election (24th November, 2010) high correlation observed between 1st preference votes and Facebook friends (r = 0.889, N = 5, p < 0.05).
  • 4. Data collection Follower/fan data collected from 21st to 24th February 2011. T-tests comparing data collected at either extreme of time period revealed no differences. 1st preference vote share was collected from the RT News election Candidates gender was inferred from their names (!) Age was sourced and cross-referenced from various reputable online sources, and later politely requested via email. Population density data was calculated from constituency area measurements gratefully supplied by Richard Cantillion of GAMMA Ltd. using D叩il Boundaries provided by Ben Raue, www.tallyroom.com.au and population statistics from the Central Statistics Office.
  • 5. Data collection Election 566 candidates 481 male, 85 female. Reliable date of birth sourced for 372 average 48.22 (range 22 75, st. dev. 11.451). 125 incumbents stood for election
  • 6. Data collection Twitter 325 accounts, average 467.52 followers (2-11465, st. dev. 1039.81, sum 151,945) hence 57.4% of all candidates were on Twitter
  • 7. Data collection Facebook 432 accounts average of 730.65 friends/fans/members (1 to 5000, st.dev. 875.10, sum 315,640) 78.8% of all candidates had Facebook accounts of one kind or another
  • 8. Data collection Facebook 432 accounts 316 Friend accounts, 112 Fanpages, and 3 were groups. 14 private Facebook accounts were also identified, but as their Friend totals were not visible, had to be removed from the final analysis. When a candidate had both a Friend and Fanpage, the page with the highest level of support, as a better indicator of popularity, was used (unless the candidate requested otherwise). NB no difference between candidates with Friend or Fanpages in terms of likelihood of getting elected (U = 17578, N1 = 317, N2 = 112, p = .850 ) nor more likely to get votes (t = .356, df = 182.019, p = .722)
  • 10. Research Questions What factors are related to having a presence on social media? being popular on social media? How does social media presence and popularity relate to votes received? Ultimately, how does social media relate to getting elected....
  • 11. U have no chance if ur not on facebook and twitter. If u can't do social media then can u do anything at all? Male, Labour, Louth
  • 12. 1. Gender Presence: Facebook Female candidates more likely to have an account 2(1, N = 566) = 4.085, p < 0.5. However weak strength association = 0.085. Twitter No difference between genders in having accounts 2(1, N = 566) = 2.929 , p = 0.087. Popularity Facebook no difference in friends/fans (t = 1.060, df = 430, p = .290) Twitter no difference in followers (t = -0.367, df = 323, p = 0.714)
  • 13. 2. Age Younger candidates expected to be more popular Facebook no negative correlation observed between a candidates age and their number of friends/fans (r = - .059, N = 310, p = > .05). Twitter no negative correlation observed between a candidates age and their number of followers (r = .073, N = 254, p = > .05). Further analyses, as regards age predicting presence on social media, did not reveal models of any significance
  • 14. 3. Party affiliation Facebook Presence differences observed across parties in terms of their candidates having a Facebook account 2(10, N = 566) = 81.280, p < 0.001, with moderate strength observed = 0.379
  • 16. 3. Party affiliation Facebook Popularity differences between the parties in terms of their candidates popularity on Facebook (F(9,422) = 6.040, p < 0.01)
  • 18. 3. Party affiliation Twitter Presence differences between parties and the likelihood of their candidates having a Twitter account 2(10, N = 566) = 86.268, p < 0.001, with moderate strength observed = 0.390
  • 20. 3. Party affiliation Twitter Popularity differences between the parties in terms of their candidates popularity on Twitter (F(9,315) = 2.454, p < 0.05)
  • 22. 4. Constituencies Facebook Presence no differences across constituencies 2(1, N = 42) = 52.129, p = 0.136. Popularity no differences across constituencies in terms of candidates popularity on Facebook (F(42, 431) = 1.399, p = 0.056).
  • 23. 4. Constituencies Twitter Presence no differences across constituencies 2(1, N = 42) = 43.743 , p = 0.397. Popularity differences across constituencies in terms of popularity on Twitter (F(42, 324) = 1.508, p < 0.05).
  • 25. 5. Urban/rural Facebook Presence no differences between urban and rural candidates and their having a Facebook account 2(1, N = 566) = 0.278, p = .598. Popularity differences between urban and rural candidates in terms of their candidates popularity on Facebook (t = 2.297, df = 366.277, p < 0.05) with a confidence interval of CI95 (27.876, 359.798).
  • 27. 5. Urban/rural Twitter Presence a difference between urban and rural candidates and their having a Twitter account 2(1, N = 566) = 3.986, p < 0.05, though with only weak strength = - 0.084.
  • 29. 5. Urban/rural Twitter Popularity statistically differences between the urban and rural candidates in terms of their candidates popularity on Twitter (t = - 2.267, df = 323, p < 0.05) with a confidence interval of CI95 (- 496.006, -35.083).
  • 31. 6. Incumbency Facebook Presence no differences between incumbent and non-sitting with regard to their having a Facebook account 2(1, N = 566) = 3.459, p = .063. Popularity statistically differences between the incumbent and non-sitting candidates in terms of their candidates popularity on Facebook (t = 3.532, df = 430, p < 0.001) with a confidence interval of CI95 (156.620, 549.525).
  • 33. 6. Incumbency Twitter Presence difference between incumbent and non-sitting candidates in terms having a Twitter account 2(1, N = 566) = 13.948, p < 0.001, though with only weak strength = .157.
  • 35. 6. Incumbency Twitter Popularity statistically differences between the incumbent and non-sitting candidates in terms of their candidates popularity on Twitter (t = - 4.126, df = 323, p < 0.001) with a confidence interval of CI95 (271.585, 766.660).
  • 37. My advice would be don't set up an account unless you are going to use it. I followed several candidates who rarely posted. And I certainly felt that many of the candidates had nothing to do with their accounts, that it was a team member. I would advise that if it is a team member, that they are upfront about it. Female, Greens, Dublin South
  • 38. 7. Votes Correlation Facebook positive correlation observed between a candidates popularity on Facebook and the number of first preference votes received (r = .450, N = 432, p < 0.01, one-tailed) Twitter positive correlation observed between a candidates popularity on Twitter and the number of first preference votes received (r = .164, N = 325, p < 0.01, one-tailed)
  • 39. 7. Votes First preferences Facebook difference between the number of first preference votes received by candidates who had a Facebook account and those who did not F(1, 562) = 6.019, p < .05. Twitter difference between the number of first preference votes received by candidates who had a Twitter account and those who did not F(1, 562) = 19.404, p < .001. but no interaction effect for having both F(1, 562) = 1.098, p = .295. i.e. an effect for one or the other, but no bonus
  • 40. 7. Votes Twitter Facebook Mean Std. Dev. N Yes Yes 4885.26 3663.938 306 No 4241.58 3486.822 19 Total 4847.63 3651.784 325 No Yes 3347.78 3032.523 140 No 1744.45 2952.871 101 Total 2675.84 3096.398 241 Total Yes 4402.65 3547.363 446 No 2139.82 3162.951 120 Total 3922.90 3588.197 566
  • 42. 7. Votes Multiple regression analysis (stepwise method) predictor variables: number of followers number of friends or fans incumbency population density constituency urbanity affiliation. model emerged F(7, 287) = 35.702, p < 0.005 which predicted 45.2% of the variance. Facebook support, incumbency and party were predictors, though the others were not.
  • 43. 7. Votes B Std. Error Beta Number of followers .029 .160 .009 Number of friends or fans 1.188 .185 .306* If they had a seat -1943.192 404.823 -.233* Constituency -.299 13.533 -.001 Urbanity 117.151 442.337 .016 Population density -.293 .145 -.120 Affiliation -380.579 45.418 -.401*
  • 44. Pre -Social media there was no way Mick Wallace could have amassed 13,000 first prefernce votes in the space of 3 weeks in Wexford. The lads running his page deserve a medal. Male, Independent, Wexford
  • 45. 8. Success Facebook those with accounts were more likely to get elected than those without 2(1, N = 566) = 14.767, p < 0.0005 . = 0.162.
  • 47. 8. Success Twitter those with accounts were more likely to get elected than those without 2(1, N = 566) = 29.947, p < 0.0005 (though = 0.230)
  • 49. 8. Success Incumbents no differences among incumbents between those who had Facebook accounts as to whether or not they won a seat 2(1, N = 125) = .775, p =.397. no differences among incumbents between those who had Twitter accounts as to whether or not they won a seat 2(1, N = 125) = .080, p = .777.
  • 50. 8. Success Challengers Facebook differences between those who had Facebook accounts as to whether or not they won a seat 2(1, N = 441) = 19.339, p = .0005. = .209
  • 52. 8. Success Challengers Twitter differences between those who had Twitter accounts as to whether or not they won a seat 2(1, N = 441) = 24.198, p = .0005. = .234
  • 54. 8. Success (Continuing to ignore incumbents, no differences) Urban Facebook Challengers who had accounts were not more likely to get elected than those who did not 2(1, N = 155) = 2.845, p = .092. Twitter Challengers who had accounts were more likely to get elected than those who did not 2(1, N = 155) = 8.114, p = .005. = .229.
  • 56. 8. Success (Continuing to ignore incumbents, no differences) Rural Facebook Challengers who had accounts were more likely to get elected than those who did not 2(1, N = 286) = 17.577, p = .0005. = .248 Twitter Challengers who had accounts were more likely to get elected than those who did not 2(1, N = 286) = 15.972, p = .0005. = .236
  • 61. A curious effect... Facebook comparing incumbents/challengers with successful/failed candidates a significant effect of success on number of friends/fans F(1, 428) = 34.323, p < .0005 but no effect of success Twitter comparing incumbents/challengers with successful/failed candidates a significant effect of success on number of followers F(1, 562) = 14.422, p < .05 but no effect of incumbency
  • 62. 8. Success Logistic regression model success in election predicted using urbanity, population density, party affiliation, number of Twitter followers, number of Facebook friends/fans, incumbency, age and gender as variables 233 cases examined predicted election (omnibus chi-square 146.37, df = 16, p < 0.0005) accounted for between 46.6 and 62.3% of the variance, with 81.8% of successful and 84.6% of unsuccessful candidates correctly predicted
  • 64. Success Interestingly, while the number of Twitter followers a candidate had does not seem to had a impact on their success, each Facebook fan or friend increased their chance of getting elected by a factor of 1.001...