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Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
So what were the conclusions?
5
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Why focus on Twitter?
10
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
How did we analyse the data?
13
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Party momentum
20
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Overall election influencers & top content
28
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
The SA elections 2014 conversation map
39
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Democratic Alliance (DA) community
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Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Economic Freedom Fighters (EFF) community
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Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Black influencers (???) community
57
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
African National Congress (ANC) community
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Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Ele...
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Using network science to understand elections: the South African 2014 national elections on Twitter

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This presentation describes our research into the roughly one million tweets that we collected in the run-up to the 2014 national elections in South Africa. It uses a mixture of network theory and data science to unpack the main communities and topics of conversation. The paper won the Gold Award for Best Paper at the 2015 SAMRA conference.

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Using network science to understand elections: the South African 2014 national elections on Twitter

  1. 1. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Using Network Science to Understand Elections: The 2014 South African National Elections on Twitter Kyle Findlay & Ockert Janse van Rensburg Winner of the Gold Award for Best Paper at the 2015 Southern African Marketing Research Association (SAMRA) annual conference
  2. 2. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 2 Let’s start by having a look at the data in action… (video animation on next slide)
  3. 3. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter In the interest of privacy, we do not report on all influencers in this paper. We stick to only reporting those that already have a significant public presence, either in the South African media (e.g. politicians) or on Twitter itself (i.e. more than 10,000 followers). This data is from Q2 2014. A lot can change in a year, including people’s opinions and political allegiances. Again, community membership should not be taken alone as proof of political views nor allegiances. A few important caveats before we begin… 3 This paper uses network theory-based approaches to identify communities of Twitter users within the data. It is important that readers understand that community membership does not 100% identify nor guarantee a user’s political views nor alignment. There are two main reasons for this: 1. Community membership is based on a non-deterministic algorithm that uses a random seed to start the community detection process. This means that community membership can be unstable and so reported memberships should be taken with a pinch of salt. 2. In simplistic terms, users are grouped into communities based on who they interact with most. Generally speaking, people tend to interact with other like-minded people; however, antagonistic interactions can also bind communities i.e. people may form part of the same community due to debates wherein users engage each other on their differing viewpoints. Community membership Privacy Time period
  4. 4. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Contents 4 1. So what were the conclusions? 2. Why focus on Twitter? 3. How did we analyse the data? 4. Party momentum 5. Overall election influencers & top content 6. The SA elections 2014 conversation map 7. Democratic Alliance (DA) community 8. Economic Freedom Fighters (EFF) community 9. Disenchanted ANC (ANC) community 10.ANC Stalwarts (ANC) community
  5. 5. So what were the conclusions? 5
  6. 6. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter President Jacob Zuma has split the ANC in two 6 Many supporters happily engage with official party mouthpieces such as @MyANC_, Sports Minister Fikile Mbalula and @ANC_Youth. However, many disenchanted millennials appear to have found their thoughts echoed by unofficial influencers such as Khaya Dlanga, @Mtshwete and @TaxiDriverSipho Support for the ANC is unequivocal amongst these groups but half of the party’s supporters do so despite of their dissatisfaction with Jacob Zuma
  7. 7. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter The 2014 national elections conversation on Twitter consisted of four main constituencies 7 DA DA EFF EFF Dis- enchanted ANC Dis- enchanted ANC ANC stalwarts ANC stalwarts …which encompassed 52% of all users in the conversation …and generated 85% of all tweets!
  8. 8. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter EFF community @City_Press @POWER987News @SABCNewsOnline @SAfmnews @Radio702 ANC stalwarts @ANN7tv @The_New_Age @SAgovnews Some news media outlets’ content resonated primarily with specific constituencies 8 DA community @RSApolitics @JacaNews @BDliveSA @dailymaverick Disenchanted ANC [No news entity appeared to cater specifically to, nor particularly resonated with, this community] Many news outlets formed their own independent communities. For example… • eNCA • EWN News • Mail & Guardian • News24 • Times Live • SA Breaking News However, these news outlets’ content primarily resonated within the following communities:
  9. 9. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter The distribution of Twitter mentions gives us tantalising clues at possible future party momentum 9 62% 22% 6% 3% 2% 1% 1% 1% 1% 1% 1% 0% 0% 55% 22% 14% 1% 0% 1% 1% 2% 4% ANC DA EFF IFP NFP FF+ UDM ACDP AIC COPE Agang APC PAC Seats in parliament % Twitter mentions If the entire country was on Twitter, would the election results have looked like this?
  10. 10. Why focus on Twitter? 10
  11. 11. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Only 13% of South Africans belong to Twitter 11 100% 46% 42% 13% 10% 7% 2% Total consumer market Total social networking users Facebook Twitter Google+ YouTube Instagram % belong to Source: TNS Sunday Times Top Brands Survey 2014
  12. 12. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 12 “BuzzFeed found that Twitter has a big cascade effect on other social media platforms. Put simply, it appears that huge stories often start as tweets, then get shared by influencers to Facebook and other networks, where the original piece of content subsequently gets far more distribution.” …however, Twitter has an outsized effect on information spread Source: http://www.fastcompany.com/3043788/sxsw/twitters-influence-problem-visualized
  13. 13. How did we analyse the data? 13
  14. 14. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 14 user location 1,461,909(3 March – 12 May 2014) user time-zone user language tweet language We started with 1.5m tweets about the elections which we cleaned extensively to remove irrelevant tweets…
  15. 15. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 15 981,878tweets in the end irrelevant hashtags irrelevant influencers irrelevant retweets k-means clustering conversation network
  16. 16. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 16 We connected users that interacted with each other
  17. 17. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 17
  18. 18. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 18 …and then ran a community detection algorithm to identify distinct communities
  19. 19. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter We ‘clustered’ tweets into topics using Latent Dirichlet Allocation (LDA) 19
  20. 20. Party momentum 20
  21. 21. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 21 248 89 25 10 6 4 4 3 3 3 2 1 1 ANC DA EFF IFP NFP FF+ UDM ACDP AIC COPE Agang APC PAC Seats in parliament A reminder of the actual election results…* * …which follow a power law (R² = 0.97)
  22. 22. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 22 62% 22% 6% 3% 2% 1% 1% 1% 1% 1% 1% 0% 0% 38% 25% 13% 3% 2% 1% 1% 1% 0% 4% 3% 0% 1% ANC DA EFF IFP NFP FF+ UDM ACDP AIC COPE Agang APC PAC % of seats in parliament % of media coverage R=0.95 Party media coverage aligned fairly closely with the actual results Source: Media Monitoring Africa Election Coverage 2014, http://elections2014.mediamonitoringafrica.org
  23. 23. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 23 62% 22% 6% 3% 2% 1% 1% 1% 1% 1% 1% 0% 0% 55% 22% 14% 1% 0% 1% 1% 2% 4% ANC DA EFF IFP NFP FF+ UDM ACDP AIC COPE Agang APC PAC % of seats in parliament % Twitter mentions R=0.99 …but each party’s share of Twitter mentions aligned even better with their actual results
  24. 24. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 24 Twitter share of mentions notably diverged from actual seats in parliament in two cases… The ANC received less than its fair share of Twitter mentions than we would expect given its final number of parliamentary seats won The EFF received more Twitter mentions that seats won The DA received exactly its fair share of mentions versus seats won
  25. 25. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 25 The results might imply that, if South African Twitter users were more representative of the voting public,… …the ANC might have received far fewer seats (62% vs. 55%) …and most of these losses might have come from the EFF which might have received a greater share of seats (6% vs. 15%) …while the DA’s voter block probably is more representative of its Twitter users, thus its share of seats neatly aligns with its share of Twitter mentions (22%) What might these results imply (with a very healthy dose of speculation)? However, it’s important to remember that this interpretation is very speculative at best!
  26. 26. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 26 The scientific debate on the predictability of Twitter mentions vs. elections is still undecided though… Source: Young-Ho, E, et al. (2015). Twitter-based analysis of the dynamics of collective attention to political parties
  27. 27. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 27 “Despite the many efforts, results are still inconclusive...” “We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window.” Source: Young-Ho, E, et al. (2015). Twitter-based analysis of the dynamics of collective attention to political parties
  28. 28. Overall election influencers & top content 28
  29. 29. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 29 39 008 34 592 31 481 27 746 12 855 12 764 10 478 10 051 9 602 8 085 7 671 7 629 6 649 6 379 6 364 6 050 5 092 5 031 4 735 4 639 @HelenZille @MyANC_ @DA_News @EconfreedomZA @ANC_Youth @Julius_S_Malema @Maimanea @MbalulaFikile @AgangSA @eNCANews @News24 @LindiMazibuko @City_Press @SABreakingNews @POWER987News @Sentletse @EWNReporter @ChesterMissing @SAPresident @TimesLive Top 20 influencers shown where influence = # interactions (retweets + mentions)
  30. 30. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 30 @HelenZille @MyANC_ @DA_News @EconfreedomZA @ANC_Youth @Julius_S_Malema @Maimanea @MbalulaFikile @AgangSA @eNCANews @News24 @LindiMazibuko @City_Press @SABreakingNews @POWER987News @Sentletse @EWNReporter @ChesterMissing @SAPresident @TimesLive Democratic Alliance African National Congress Economic Freedom Fighters News media Top 20 influencers shown where influence = # interactions (retweets + mentions)
  31. 31. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 31 10 6 18 15 21 20 16 29 The top hashtags give us some insight into the most prevalent topics (see hashtags highlighted with arrows) Top 30 hashtags. Excludes “elections”- and “South Africa”-related hashtags Numbers inside arrows represent rank of hashtag in term of frequency of occurrence in data
  32. 32. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 32 What was the top 20 most retweeted content?
  33. 33. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Author Retweet content # retweets @justicemalala Mbeki's #ANC in 99: 66.35% Mbeki's ANC 2004: 69.6% Zuma's ANC 2009: 65.9% Zuma's ANC 2014: 62.84 (22.12/08 May) #justsay… 705 @Trevornoah Jacob Zuma is a great. I bet he did this Nkandla thing just to unite all South Africans in a common anger at corruption. 590 @ProudlySA Congratulations to Deputy President Motlanthe & his beautiful bride, Gugu on their wedding today!! @PresidencyZA http://t.co… 524 @IECSouthAfrica National Assembly seats: APC–1; PAC–1; AGANG SA–2; ACDP–3; AIC–3; COPE –3; UDM–4; VF Plus–4; NFP–6; IFP–10; EFF–25; DA–… 490 @helenzille By saying that "only clever people" have problems with R246-mill Nkandla upgrade, Pres Zuma is implying that ANC voters are… 394 @[] @IECSouthAfrica apparently found in home of ANC party agent. https://t.co/OyoYIvI79Y 346 @alexeliseev Zuma on #Nkandla: "It's not an issue with voters. It's an issue with bright people. Very clever people". What is he saying… 344 @Sentletse The ANC and the IEC must be ashamed of themselves! http://t.co/STwypvLg8M 342 @IECSouthAfrica I.E.C voting material found at a house of an anc party agent in ward 77 http://t.co/48MAjhqqXE" 326 @[] EFF votes found dumped near Diepsloot cc @Julius_S_Malema @Sentletse @EconFreedomZA with @IECSouthAfrica stamp http://t.co/mIzvrow… 325 @Julius_S_Malema We will soon announce the date of the march to the union buildings to demand Zuma's resignation as the president 294 @helenzille Monitor the polls!! "@E_van_Zyl_17: "@JohnBiskado: IEC voting material found at house of anc party agent ward 77 http://t.… 289 @MbalulaFikile But you cant use the same BIS bought with NSFAS to tweet "ANC HAS DONE NOTHING". Uxokelani? 280 @GarethCliff Oh no! RT @DonnyDunn: @GarethCliff @KienoKammies IEC integrity obliterated! http://t.co/wILOizXGXm 279 @MbalulaFikile Biggest loser of the century DR Mamphela Ramphela ,u Luzile shameeee 272 @TannieEvita Confusius say: "He who knows nothing about his own house, knows even less about his own country." @SAPresident 271 @GarethCliff Who told @helenzille it would be a good idea to do this? Will she campaign in blackface next? http://t.co/TzbwsqgHGL 258 @GarethCliff Even if you didn't vote ANC, they will form your new govt. wishing them luck is wishing the best for all of us. Good luck … 268 @MaxduPreez Defend this, ANC: Public Works Dept redirected service delivery funds to pay for Nkandla in contravention of constitution 245 @MbalulaFikile Yet you're on Twitter and can write that in perfect English, let's face it, you're ANC's good story. @lusylooya: ANC ha… 238
  34. 34. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 34 And if we group the retweets by theme?
  35. 35. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Ballot tampering Nkandla Political smack talk Fikile Mbalula and the ANC’s ‘good story’ Election results & well wishing Deputy President Motlanthe‘s wedding Malema march for Zuma’s resignation
  36. 36. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Many of the top most shared images related to alleged ballot tampering 36
  37. 37. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 37 This image drawing a parallel between President Jacob Zuma’s Nkandla scandal to e-tolling was one of the most popular
  38. 38. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 38 …as was this image critical of DA leader, Helen Zille
  39. 39. The SA elections 2014 conversation map 39
  40. 40. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter The overall elections conversation map 40 The various regions of colour clearly highlight distinct communities in the elections conversation. Specific influential accounts that led the conversation are clearly visible within each community, hinting at the agenda of each.
  41. 41. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 41 Black influencers ? EFF DA (and FF+) ANC eNCA & EWN News International news media Gareth Cliff & Ulrich J van Vuuren Comedians Agang News24 & TimesLive SA Breaking News IEC & PresidencyZA Mail & Guardian DJ Sbu The top four communities encompass 52% of unique users and appear to relate to political parties. Other communities relate to news entities, DJs, comedians, etc.
  42. 42. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 42 Black influencers ? EFF DA (and FF+) ANC However, the top four communities generated almost all of the tweets about the elections (85%)!
  43. 43. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 43 It was not initially clear what the “black influencers” community stood for. It required some digging into their actual tweet topics to find out more… Black influencers ???
  44. 44. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 44 …so let’s unpack the top four communities in more detail…
  45. 45. Democratic Alliance (DA) community 45
  46. 46. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 46 Mabine Seabe II @Mabine_Seabe Lindiwe Mazibuko @LindiMazibuko Helen Zille @helenzille Democratic Alliance @DA_News Mmusi Maimane @MaimaneAM* RSApolitics @RSApolitics Jacaranda News @JacaNews ToxiNews @toxinews Gavin Davis @gavdavis Influencers Top influencers ranked on # interactions (retweets + mentions). * Username has changed subsequently. Original account hacked?
  47. 47. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 47 BDlive @BDliveSA RSApolitics @RSApolitics Jacaranda News @JacaNews ToxiNews @toxinews Daily Maverick @dailymaverick Particularly resonant media entities These news media accounts’ content resonated with this community more than any other (most mentions and retweets)
  48. 48. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 48 Nkandla & Zuma corruption Ballot tampering Zille pro (e.g. made the party) Zille against (e.g. Twitter meltdown) Mazibuko for president Maimane for president ANC “good story” (sarcastic) Topics of conversation Summary based on top retweeted content and LDA topic models
  49. 49. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 49 Top shared media
  50. 50. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 50 Top shared media
  51. 51. Economic Freedom Fighters (EFF) community 51
  52. 52. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Redi Tlhabi @RediTlhabi POWER987 News @POWER987News EFF Official Account @EconFreedomZA Julius Sello Malema @Julius_S_Malema City Press Online @City_Press Sentletse @Sentletse SABC News Online @SABCNewsOnline Ranjeni Munusamy @RanjeniM Carien du Plessis @carienduplessis 52 Influencers Top influencers ranked on # interactions (retweets + mentions).
  53. 53. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 53 SAfm news @SAfmnews 702 @Radio702 City Press Online @City_Press POWER987 News @POWER987News SABC News Online @SABCNewsOnline Particularly resonant media entities These news media accounts’ content resonated with this community more than any other (most mentions and retweets)
  54. 54. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 54 Ballot tampering March for Zuma’s resignation ANC decline under Zuma ANC’s “good story” (sarcastic) Armed Bekkersdal ANC supporter Topics of conversation Summary based on top retweeted content and LDA topic models
  55. 55. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 55 Top shared media
  56. 56. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 56 Top shared media
  57. 57. Black influencers (???) community 57
  58. 58. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 58 Trev @Tokyo_Trev Siya @Siya_THATguy* Khaya Dlanga @khayadlanga Mayihlome @MTshwete IG: TaxiDriverSipho @TaxiDriverSipho Nzinga @NzingaQ I.G: Questionnier @Questionnier L’Vovo Derrango @LvovoSA DJ Lulo Cafe @LuloCafe Influencers Top influencers ranked on # interactions (retweets + mentions). NOTE: some users with <10k followers not shown for privacy reasons * Username has changed subsequently. Original account hacked?
  59. 59. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 59 N/A No media entities resonated primarily with just this community
  60. 60. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 60 Long live the ANC ANC’s good story ANC’s decline under Zuma No respect for Zuma Voting ANC (some voting DA) Congrats to Lindiwe Mazibuko Zille ‘trying too hard’ Exasperation with EFF (and some support) When we unpacked their topics of conversation, it became clear what defined them – their disappointment in Jacob Zuma! Summary based on top retweeted content and LDA topic models
  61. 61. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 61 Top shared media
  62. 62. African National Congress (ANC) community 62
  63. 63. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 63 ANN7 24-hour news @ANN7tv Malusi Gigaba @mgigaba ANC Info Feed @MyANC_ ANC Youth League @ANC_Youth* Fikile Mbalula @MbalulaFikile ANC-HISTORY @ANC_LECTURES Vote @MyANC_!!!!! @anccadres ANC Gauteng @GautengANC The New Age @The_New_Age Influencers Top influencers ranked on # interactions (retweets + mentions). NOTE: some users with <10k followers not shown for privacy reasons * Username has changed subsequently. Original account hacked?
  64. 64. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 64 ANN7 24-hour news @ANN7tv The New Age @The_New_Age SA Gov News @SAgovnews Particularly resonant media entities These news media accounts’ content resonated with this community more than any other (most mentions and retweets)
  65. 65. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 65 Love ANC Love Zuma ANC’s “good story” Topics of conversation Summary based on top retweeted content and LDA topic models
  66. 66. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter 66 Summary based on top retweeted content and topic models No specific media was particularly popular within this community within our data
  67. 67. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter Thank you

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