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.

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. 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. 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. 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. So what were the conclusions?
5

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. 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. 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. 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. Why focus on Twitter?
10

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. 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. How did we analyse the data?
13

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. 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. 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. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
17

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. 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. Party momentum
20

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. 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. 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. 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. 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. 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. 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. Overall election influencers & top content
28

29. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
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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. 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. 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. 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. 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. 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. 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. 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. 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. 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. The SA elections 2014 conversation map
39

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. 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. 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. 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. 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. Democratic Alliance (DA) community
45

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. 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. 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. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
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Top shared media

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. Economic Freedom Fighters (EFF) community
51

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. 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. 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. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
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Top shared media

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. Black influencers (???) community
57

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. 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. 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. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
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Top shared media

62. African National Congress (ANC) community
62

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. 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. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
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Love ANC
Love Zuma
ANC’s “good story”
Topics of conversation
Summary based on top retweeted content and LDA topic models

66. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
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Summary based on top retweeted content and topic models
No specific media was particularly popular within this
community within our data

67. Kyle Findlay & Ockert Janse van RensburgUsing Network Science to Understand Elections: The 2014 South African National Elections on Twitter
Thank you