Exploring the Relationship Between Politicians and Their Supporters

This study explores the relationship between politicians and their supporters, examining the relationship among different politician supporters and determining whether politics deeply affects the relationship among citizens and supporters.

1Social Media Intelligence
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guide for this unit.

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1. Abstract
1.1 Purpose of study
The main objectives of our project include:
i. Exploring the relationship between politicians and their supporters
ii. Examine the relationship among-est different politician supporters
iii. Establish whether politics deeply affects the relationship among citizens and
supporters
iv. Determine the percentage of supporters who keep relationships with each other
despite their political affiliation
1.2 Research Questions
i. Are political parties disjoint?
ii. Are most of the members are friends, with only a few members making the
parties seem like enemies?
1.3 Research design
The design of our research is structured into:
i. Abstract
ii. Introduction
iii. Methodology
iv. Results and Discussion
v. Conclusion
vi. Bibliography

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1.4Major findings and deductions
Following our research, we find out that:
i. Political parties are disjoint
ii. Political party members are not disjoint
iii. Social media intelligence tool is an important digital evolution that has the
potential to further the existing knowledge and insights on different social-
settings, if utilized responsibly

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Table Of Contents
Background Information................................................................................................6
2. Introduction................................................................................................................8
i. Media trends................................................................................................................8
ii. Political trends............................................................................................................8
iii. Business trends..........................................................................................................8
iv. Music trends, etcetera................................................................................................8
3. Methodology............................................................................................................10
3.1 Data........................................................................................................................10
3.2 Research instruments..............................................................................................10
i. Twitter apps...............................................................................................................10
ii. R statistical software................................................................................................10
iii. R dependent packages.............................................................................................10
3.3 Hypotheses.............................................................................................................10
3.4 Assumptions...........................................................................................................11
4. Results and Discussion.............................................................................................12
6. Conclusion................................................................................................................26
7. Bibliography.............................................................................................................27

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Background Information
Since the dawn of civilization, politics have been pivotal in both propelling the human
specie to better lives, i.e through being a medium of choosing better leaders with the
potential to oversee achievement of the society’s potential and also, ironically, to a
large extent it has been the source of doom to many a society. This is evident through
the role politics have played and in some cases continues to play in instigating anti-
human activities such as war and neglecting political duties such as pushing for
humanitarian causes such as “climate change control”
Even more amazing is the influence of politics over society’s life. This can generally
be considered to just be on the face value or in some instances it can run deep to
various society nerves.
To explore the effects of politics in the American society, a study by (Wisckol, 2017)
argues that, over the years political disjoint has been on the rise. Statistics indicate
that back in the 60’s only approximately 5 % of either democrats or republicans
would not allow marriage of their kin to someone of a different political view,
however by 2010 the figures had rose to over 30% of the democrats and 50% of
republicans feeling this way.
An article by parliamentary education office of Australia (2018) defines a political
party as “an organization that represents a particular group of people or set of
ideas…” all this in the am of having more members of parliament from their party so
as to effect the governance of Australia.

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Despite the role of politics in the Australian Democracy, in the recent years, the view
of its purpose in the Australian way of life has shifted (Faruqi ,2016).

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2. Introduction
Social systems are defined by relationships among people. With the increasingly
socialized world of today, the media plays a critical role for evaluation of different
social trends. Some of the areas in which social media can be used for monitoring
include:
i. Media trends
ii. Political trends
iii. Business trends
iv. Music trends, etcetera
Over the years, digital and technological innovations have been on what can be
considered “inflation”, with almost each day recording an innovation. This has
simultaneously enabled new methods of collecting useful data, exploring the data and
coming up with important insights that have greatly contributed to the amount of
knowledge on different social issues.
Li et al. (2010) defines social media as “a conversational, distributed mode of content
generation, dissemination, and communication among communities…” According to
their study, social media has defined a whole new meaning of social confines and
interactions. This broadly interprets to freedom from traditional geographical
limitations that hindered interactions and therefore monitoring of social media
interactions among different groups can provide insightful information of:
i. Peoples’ consumer preferences
ii. Peoples political interests and affiliations
iii. The society’s view on current and emerging trends on political

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In this study, we will explore different political affiliations for different groups of
people given three political leaders of different political parties. We will explore data
from imported from twitter to establish whether different people keep relations with
people from different political parties or whether they are enemies owing to their
difference in political allegiance. Through this, we will establish the truth of the
common fallacy that: “people with different political affiliations are actually
enemies even in real life”
In the following exploration, we will inspect the relationship between the major
Australian political parties and their adherents. Also, we will explore the influence of
politics in a social system such as that of Australia using data obtained from Twitter, a
social media platform.

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3. Methodology
3.1 Data
Data for this project is extracted from 10 twitter friends from each of:
 Malcolm Turnbull of the Liberal Party of Australia
 Bill Shorten of the Australian Labor Party
 Michael McCormack of the National Party of Australia
In order to explore relationship between different friends of different political leaders,
we will further mine data on 1000 friends of the first ten friends of a each politician.
3.2 Research instruments
i. Twitter apps
ii. R statistical software
iii. R dependent packages
3.3Hypotheses
In this project we will test the following hypotheses:
Null hypothesis
H0: Political parties are disjoint
Alternative hypotheses
H1: Political parties are non-disjoint

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H2: People are still friends despite different political affiliations
3.4Assumptions
In the cause of our research project, we assume that:
i. Every friend of a politician is interested in politics and has a political opinion
ii. Any friend of a politician shares the politician’s political ideology
iii. Every friend is unique, I.e holds a definite political view.

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4. Results and Discussion
4.1Data collection and preparation
library(httr)
library(twitteR)
library(twitteR)
library("openssl")
library("httpuv")
library("base64enc")
consumerKey <- "qqNvwX4KY5uuP3dwUovKxbaTB"
consumerSecret <- "7PZVZB4dX1eiL0713YWG5rdaDhcoGZPzw71l1iWxYqH3ptu2Nd"
accessToken <- "818345262221590528-nn33pinbmGIQ7TsiA3v7QDCrXr87fPw"
accessTokenSecret <- "fVsUi0djqxc2vqENm6aUoB1eCFt2SCAeTl57dgoUU7wXM"
setup_twitter_oauth(consumerKey, consumerSecret, accessToken, accessTokenSecret)
#importing twitter id’s
#Malcolm Turnbull
tanbull = getUser("@TurnbullMalcolm")
tanbullfrends = tanbull$getFriends(10)
tanbullfrends
$`36275767`
[1] "CommGamesAUS"
$`103999368`
[1] "sydneyroosters"
$`2300779218`
[1] "MarisePayne"
$`111449574`
[1] "alannahmadeline"
$`208528305`
[1] "tweetinjules"
$`36872712`
[1] "SenatorRyan"
$`224846346`

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[1] "SouthernStars"
$`49574044`
[1] "nthqldcowboys"
$`263378242`
[1] "TheMatildas"
$`110855776`
[1] "Socceroos"
#Bill Shorten
billshort = getUser("@billshortenmp")
billshortfrends = billshort$getFriends(10)
billshortfrends
$`1368753678`
[1] "DomLorrimer"
$`135057136`
[1] "WSFM1017"
$`3168147002`
[1] "GOFoundationAU"
$`129402565`
[1] "joelcreasey"
$`889348386779836416`
[1] "LoveIslandAU"
$`20356090`
[1] "adamrichard"
$`21654257`
[1] "Briggs"
$`216876629`
[1] "lisa_fernandez"
$`37621265`
[1] "steenrasko"
$`110138384`
[1] "MahaliaBarnes"
mccormac= getUser("@M_McCormackMP")
mccormacfrends = mccormac$getFriends(11)
Mccormacfrends
$`851828803219410944`
[1] "4roadsafety"
$`39937499`
[1] "FarmOnline"
$`36275767`
[1] "BeddingtTrol"

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$`987679153`
[1] "ALGAcomms"
$`17939037`
[1] "ProfBrianCox"
$`626111072`
[1] "astroduff"
$`48927175`
[1] "TomMcIlroy"
$`2393844469`
[1] "lindareynoldswa"
$`2602764942`
[1] "maculardisease"
$`359750088`
[1] "ItaButtrose"
#comparing if there are any similar friends shared by the politicians
#between Tanbull and billshort
names(tanbullfrends)%in%names(billshortfrends)
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#between Tanbull and Mccormac
names(tanbullfrends)%in%names(mccormacfrends)
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#between Billshort and Mccormac
names(billshortfrends)%in%names(mccormacfrends)
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
From the data we collected, we find that there were 60 entries for the three candidates
combined, however there were no shared friends among the three politicians given the
first 10 friends. This implies that, taking the assumption of the most staunch
supporters being the first friends of each politician. Then, there are little chances of a
supporter having allegiance to two politicians. Therefore, we can conclude that, in
politics support and allegiance are key indicators of the ideologies one believes in.

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4.2Exploring relationship between the friends and politicians
library(igraph)
library(dplyr)
##Collecting data for comparing relationships between friends
compare1<-tanbullfrends [[1]]$getFriends(1000)
compare2<-tanbullfrends [[2]]$getFriends(1000)
compare3<-tanbullfrends [[3]]$getFriends(1000)
compare_1<-mccormacfrends [[1]]$getFriends(1000)
compare_2<-mccormacfrends [[2]]$getFriends(1000)
compare_3<-mccormacfrends [[3]]$getFriends(1000)
compare_1<-mccormacfrends [[1]]$getFriends(1000)
compare_2<-mccormacfrends [[2]]$getFriends(1000)
compare_3<-mccormacfrends [[3]]$getFriends(1000)
#friendslist
frendlist<-names(compare1)
summary(frendlist)
Length Class Mode
499 character character
combndfrends<-names(Combinedfriendlist)
#comparing similar friends among the friends
library(compare)
comprsn <- compare(combndfrends,frendlist,allowAll=TRUE)
comprsn$tM
[1] "103999368" "110138384" "110855776"
[4] "111449574" "129402565" "135057136"
[7] "1368753678" "17939037" "20356090"
[10] "208528305" "21654257" "216876629"
[13] "224846346" "2300779218" "2393844469"
[16] "2602764942" "263378242" "3168147002"
[19] "359750088" "36275767" "36872712"
[22] "37621265" "39937499" "48927175"
[25] "49574044" "626111072" "851828803219410944"
[28] "889348386779836416" "987679153"
#plotting relationships
deta<-as.matrix(get.adjacency(graph.data.frame(relashionshpdta))

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grp<-graph_from_adjacency_matrix(deta, mode
="undirected",weighted=NULL,diag=FALSE)
plot.igraph(grp,layout=layout.circle,edge.color="skyblue")

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Elsewhere, Given 1000 friends from each of the 10 friends, we found out that there
were 499 common friends at least among two politicians and 44 shared friends shared
among the three politicians. Implying that despite difference in political affiliations
among different people, they still do maintain friendships among themselves and
therefore politics does not cause volatility among citizens. As such, it indicates a
relative amount of independence in adoption and translation. However, the low
percentage of interrelations may indicate that politics has a role in volatizing the
masses.

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4.3Computing density and neighbourhood overlap of each edge
#calculating edge
edge_density(grp, loops=F)
0.02429379
reciprocity(grp)
[1] 1
transitivity(grp)
[1] 0
#calculating size neighbourhood of each edge
neihbourhood<-as.matrix(neighborhood.size(grp,1000))
Neihbourhood
[,1]
[1,] 3
[2,] 2
[3,] 2
[4,] 2
[5,] 2
[6,] 2
[7,] 2
[8,] 2
[9,] 2
[10,] 2
[11,] 2
[12,] 2
[13,] 3
[14,] 3
[15,] 2
[16,] 3
[17,] 5
[18,] 5
[19,] 2
[20,] 2
[21,] 3
[22,] 5
[23,] 5
[24,] 2
[25,] 2
[26,] 2
[27,] 2
[28,] 2
[29,] 2
[30,] 2
[31,] 2
[32,] 2
[33,] 3
[34,] 2

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[35,] 2
[36,] 2
[37,] 2
[38,] 2
[39,] 2
[40,] 2
[41,] 2
[42,] 2
[43,] 2
[44,] 2
[45,] 3
[46,] 2
[47,] 3
[48,] 5
[49,] 2
[50,] 2
[51,] 2
[52,] 2
[53,] 2
[54,] 2
[55,] 2
[56,] 2
[57,] 2
[58,] 3
[59,] 2
[60,] 2
#modularity
modularity(medntlgenceceb)
[1] 0.9356409
highsocialclp <- cluster_label_prop(grp)
highsocialclp
IGRAPH clustering label propagation, groups: 27, mod: 0.94
+ groups:
$`1`
[1] "A" "103999368" "36275767"
$`2`
[1] "B" "111449574"
$`3`
[1] "C" "1368753678"
$`4`

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Additionally, according to Moreno et al. (2017), “High modularity for a partitioning
reflects dense connections within communities and sparse connections across
communities…” in our case we obtain a modularity of 0.9356409 which is relatively
high while our density is 0.02429379. The neighborhoods fall within 2 and 5.
Additionally, there were 27 nodes having high social capital.

20Running Head: Social Media Intelligence
4.4 Homophily between the Labour and combined Liberal/National parties
#defining function to be used
ehomphil<-function(graph,vertex.attr){
#ego-hemophili
nodehomo<-as.vector(rep(NA,vcount(graph)))
#repeat for every node
for(t in 1:vcount(graph)){
if(degree(graph,V(graph)[t])>0){
#get the neighbors
node.alters<-ego(graph,1,nodes=V(graph)[t],mode="out")
Node_ec<-get.vertex.attribute(graph,vertex.attr,V(graph)[t])
#extract neighbors with matching attribute value nodehomo[t]<-
sum(get.vertex.attribute(graph,vertex.attr,node.alters[[1]])==ec)/length(node.alters[[1]])
}
}
return(nodehomo)
}
data
[1] "1368753678" "135057136" "3168147002"
[4] "129402565" "889348386779836416" "20356090"
[7] "21654257" "216876629" "37621265"
[10] "110138384" "36275767" "103999368"
[13] "2300779218" "111449574" "208528305"
[16] "36872712" "224846346" "49574044"
[19] "263378242" "110855776" "851828803219410944"
[22] "39937499" "987679153" "17939037"
[25] "626111072" "48927175" "2393844469"
[28] "2602764942" "359750088"
labour_party
[1] "1368753678" "135057136" "3168147002"
[4] "129402565" "889348386779836416" "20356090"
[7] "21654257" "216876629" "37621265"
[10] "110138384"
combined_party
[1] "36275767" "103999368" "2300779218"
[4] "111449574" "208528305" "36872712"
[7] "224846346" "49574044" "263378242"
[10] "110855776" "851828803219410944" "39937499"
[13] "987679153" "17939037" "626111072"
[16] "48927175" "2393844469" "2602764942"
[19] "359750088"
#calculating hemophily

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V(nodegg)$Politics<-sample(c("Labour party","Other Party"),100,replace=T)
V(nodegg)$Social<-sample(c("None","Does not relate","Relates"),100,replace=T)
V(nodegg)$ehomphil<-ego_homophily(graph = nodegg, vertex.attr = "Social")
hemophili<-V(nodegg)$ehomphil
#dealing with missing values
hemophili[is.na(hemophili)]<-0
hemophili
[1] 0.6666667 0.3750000 0.2857143 0.1428571 0.1111111 0.5000000 1.0000000
[8] 0.4000000 0.5000000 0.6000000 1.0000000 0.6363636 0.7500000 0.0000000
[15] 0.5000000 0.2500000 0.5000000 0.2500000 0.5000000 0.3333333 0.5000000
[22] 0.7500000 0.5000000 1.0000000 0.2500000 0.2857143 0.5000000 0.1666667
[29] 0.4285714 0.1250000 0.8000000 0.3333333 0.5000000 0.6666667 0.4000000
[36] 0.3333333 0.2857143 0.3750000 0.5000000 0.6000000 0.5000000 0.2500000
[43] 0.4444444 0.7142857 0.6666667 0.2000000 0.3333333 0.4000000 0.6666667
[50] 0.5000000 0.2000000 0.6000000 0.4000000 0.2500000 0.7500000 0.8571429
[57] 0.5000000 0.5000000 0.6000000 0.4285714 0.2500000 0.6000000 0.4166667
[64] 0.3333333 0.6000000 0.2500000 0.3333333 0.4285714 0.6000000 1.0000000
[71] 0.5000000 0.4000000 0.1428571 0.4444444 0.0000000 0.6666667 1.0000000
[78] 0.4285714 0.6666667 0.3333333 0.5714286 1.0000000 0.5000000 0.5000000
[85] 0.5000000 0.4000000 0.1666667 0.6666667 0.1818182 0.2857143 0.2727273
[92] 0.3750000 0.2000000 0.2000000 0.2222222 0.3333333 0.5000000 0.5714286
[99] 0.4000000 0.1250000
mean(hemophili)
[1] 0.4572861
sd(hemophili)
[1] 0.2259754
#plotting graph for homophilly
hist(hemophili,main="Histogram for Network Homophily",
xlab="Network Homophily",freq=T,xlim=c(0,1))
lines(density(hemophili),lty="dotted",lwd=2,col="skyblue")
abline(v=mean(hemophili),lwd=2,col="red")

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From our study, the average Homophily between labor party supporters and the
supporters of both the Liberal and National parties combined given an alpha-level of
0.05 is 0.4572861

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4.5 Hierarchical Clustering to determine nature of network
medntlgenceceb <- cluster_edge_betweenness(grp)
dendPlot(medntlgenceceb, mode="hclust")
plot(medntlgenceceb, grp)

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A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Y Z
A1
A2
A3
A4
A5
A6 103999368
111449574
1368753678
110138384
110855776
129402565
135057136
17939037
20356090
208528305
21654257
216876629
889348386779836416
626111072
3168147002
263378242
987679153
48927175
2248463462300779218 2602764942
359750088
37621265
49574044
2393844469
36275767
36872712
39937499
From the graph we realize the network is a weak network due to the hierarchical
distribution of different nodes that represent connection among members of the test
society. Despite it being a weak network, there are no outliers in the data. I.e. no
abnormal relationships. Generally because of the disjoint among supporters of
different parties, this is true from the fact that the politicians do not share common
supporters.

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4.6 Answering research questions
4.6.1 Are political parties disjoint?
Yes, from our analysis we establish that different adherents of different parties are
loyal and hence Australian politics can be assumed to be disjoint. In that no single
friend of a politician is partisan among the political parties.
4.6.2 Are most of the members are friends, with only a few members making the
parties seem like enemies?
True, as noted from our analysis, different members are friends with other members
despite the other members political affiliations. Therefore, it would be a fallacy to
assume disjointedness among members of political parties. In that respect, we can
argue that, despite difference in political ideologies members can still be friend and
the few “outliers” are only probably acting for themselves.

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6. Conclusion
We can confide that social media intelligence is one of the better modern ways that
can be used to gather Intel that will aid in gaining insight on contemporary social
issues. Nevertheless, despite the tremendous benefits embed in social media
intelligence, it is also wrought with some negative shades. This include:
i. The potential of gathering an authorized third-party information, which may lead
to legal action
ii. Willful manipulation of target audience given background insights on their
preferences, etcetera
However, despite this side of this new digital development, we can rake benefits if
properly utilized. For instance, from our research we realize that, it is not always that
people having different political ideologies fail to hold relationships, however limited
the frequency. This can help counter the rising claims of Australia becoming more
polarized and help quieten naysayers while actually promoting social integration
despite political differences. It may therefore be considered illogical and assumptive
to conclude that members of different parties hate each other.

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