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

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Analysis of the Candidate.ie project by Dr Ciarán Mc Mahon of Dublin Business School - Social media usage by candidates in the 2011 Irish General Election

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Candidate.ie


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




            Dr Ciarán 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 RTÉ 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 Dáil 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
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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 Ciarán 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 RTÉ 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 Dáil 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 χ2(1, N = 566) = 4.085, p < 0.5. However weak strength association φ = 0.085. – Twitter • No difference between genders in having accounts χ2(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 candidate’s age and their number of friends/fans (r = - .059, N = 310, p = > .05). – Twitter • no negative correlation observed between a candidate’s 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 – χ2(10, N = 566) = 81.280, p < 0.001, with moderate strength observed φ = 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 χ2(10, N = 566) = 86.268, p < 0.001, with moderate strength observed φ = 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 χ2(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 χ2(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 χ2(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 χ2(1, N = 566) = 3.986, p < 0.05, though with only weak strength φ = - 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 χ2(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 χ2(1, N = 566) = 13.948, p < 0.001, though with only weak strength φ = .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 candidate’s 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 candidate’s 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 χ2(1, N = 566) = 14.767, p < 0.0005 .φ = 0.162.
  • 47. 8. Success • Twitter – those with accounts were more likely to get elected than those without χ2(1, N = 566) = 29.947, p < 0.0005 (though φ = 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 χ2(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 χ2(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 χ2(1, N = 441) = 19.339, p = .0005. φ = .209
  • 52. 8. Success • Challengers – Twitter • differences between those who had Twitter accounts as to whether or not they won a seat χ2(1, N = 441) = 24.198, p = .0005. φ = .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 χ2(1, N = 155) = 2.845, p = .092. – Twitter • Challengers who had accounts were more likely to get elected than those who did not χ2(1, N = 155) = 8.114, p = .005. φ = .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 χ2(1, N = 286) = 17.577, p = .0005. φ = .248 – Twitter • Challengers who had accounts were more likely to get elected than those who did not χ2(1, N = 286) = 15.972, p = .0005. φ = .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...