Muhammad Umer Gurchani University of Montpellier Finding Evidence For

Muhammad Umer Gurchani

University of Montpellier

Finding evidence for Partisan Reinforcement through graph analysis of political rivalries

Abstract

The aim of this study is find out if Twitter users choose to expose themselves to diverse set of opinions on political matters or if they choose to listen to only one of the political adversaries, whom they already support in real life as proposed by theories of partisan reinforcement and the classic study of 1940-44 in Erie county [ CITATION Pau44 \l 1033 ]. This is a comparative study of two political rivalries on twitter; the first one is from USA and the other one from Pakistan.

Network-Graphs are created from both rivalries to try to see if clusterization happens in a way that it will create an information bias in favor of one of the rivals. For the purpose of this study, I will only be focused on ‘follow’ relationship on twitter for the reason that it is an indicator of choice for twitter users who want to expose to themselves to ideas being promoted through

Twitter accounts of these Politcians[ CITATION Mye14 \l 1033 ]. I further aspire to find out if the balkanization effect of internet is evident on twitter[ CITATION Als05 \l 1033 ]. Introduction

Twitter is one of the main social media websites on internet with an active and a vibrant user- base of about 310 million users[ CITATION 1620 \l 1033 ]. It is quickly becoming one of mainstream information sources in both politics and entertainment industry. It is therefore hard to ignore the impact of twitter on political arenas all around the world. Since twitter is essentially a media-source, so a more direct approach to such an inquiry would be to ask oneself if twitter allows users to be exposed to all sides of arguments in a political debate or not. The possibility of

‘birds of feathers tweeting together’[ CITATION Him13 \l 1033 ] and polarization is very real as suggested by Sunstein [ CITATION Sun06 \l 1033 ]. The aim of this study is to inquire further into this area with a special focus towards ‘follow’ relationship among twitter users. Two political rivalries (one from a USA and the other one from Pakistan) will be used to get a clearer picture.

The inquiry will look into the question, if political rivals have clusters of followers around them and thus preventing the followers from being exposed to the ideas of other political rival. The essence of the question could be stated by asking if twitter user allow themselves to engage in cross-political debate by following both politicians in a particular political rivalry.

Literature review

The arrival of age of internet carried with it a promise of a more democratic public sphere, with extended means to voice one’s political opinion and to know diverse set of opinions on otherwise taboo topics[ CITATION Pap08 \l 1033 ]. As time went on and we found out new dynamics of this technology and the means to control the internet became widely available (especially for the governments) , it became more and more relevant to ask the question, whether internet is adapting itself to tradition political culture or generating an entirely novel sphere of politics?.

One might think that why it became so important to inquire into internet so much, as it is an extension of media technological advancements like TV or Radio. The main reason would be that internet allows the users to choose the content they want to be exposed to [ CITATION Sun06 \l

1033 ]. This matter of choice brings an entirely new angle to interaction of media and politics.

Pessimists argued that this choice allowed the users to deliberately blindfold themselves to divergent political opinions and thus decreased the possibility of cross-political interaction

(Sunstein, 2006). On the other hand, some scholars have shown another side of the picture where internet has provided voice to individuals who were at a disadvantage in the previously existing means of communication due to multiple factors and thus increased the cross-political communication[ CITATION McK14 \l 1033 ]. Shapiro went on to suggest that internet has helped in overcoming the traditional information gatekeepers in conventional media and allowed the conversational democracy to prevail in modern times [ CITATION Sha \l 1033 ] .

The debate became interesting with the arrival of web 2.0, which completely eliminated the technical barriers for users and allowed everyone to express his opinion and comment on the material freely without any need for buying a server-space or domain name. Through this, the internet arguably achieved a maturity that it previously lacked and became a place for open debate for political thinkers. The ability to comment and counter-comment on a subject brought the internet world closer to the real world debate except that internet world had gotten rid of traditional information –gatekeepers. Anonymity allowed people to be more open to expressing ideas without caring for consequences and thus for the optimists it enriched the debates and allowed people to communicate the ideas that they would have been reluctant to say in traditional medias[ CITATION Utz15 \l 1033 ]. While the pessimists have considered it to be a means of communication which allowed people to express without any responsibility[ CITATION Ang14 \l

1033 ]

Homophily is the principle that a contact between similar people occurs at a higher rate than among dissimilar people. In real world this phenomenon occurs quite frequently, people prefer to hangout, marry, socialize and interact with people from similar background, and who have similar opinion about the major political or social ideas[ CITATION McP01 \l 1033 ] . It was hard for social scientists and literary figures not to notice this phenomenon and its observation appears in many significant texts in the world.

“Friendship ... is born at the moment when one man says to another "What! You too? I thought that no one but myself . . .” [ CITATION Lew71 \l 1033 ]

“Similarity Begets Friendship”[ CITATION Pla12 \l 1033 ]

While on one hand internet allowed people to be exposed to many people from different backgrounds and different socio-economic settings, it also gave them an option to be more efficiently in contact with people from similar backgrounds and thus create a reflection of the real world where people tend to be attracted towards people with similar social background.

Alstyne has called this phenomenon “balkanization” [ CITATION Als05 \l 1033 ]. Diana C. Mutz has proved that individual choice is a key factor, which determines if an average person chooses to expose themselves to dissimilar political opinions using traditional mainstream information sources such as newspapers and TV [ CITATION Mut01 \l 1033 ].

Before internet, geographic proximity was one of the major defining factors in the choice regarding the people you meet. Thus, people had a limitation in their choice of interaction but with increasing developments in the Internet, this limitation is no longer there and people have more freedom in choosing their interactions. Thus, homogeneity of contact is more likely to happen in this age of internet than it was previously likely.[ CITATION Als05 \l 1033 ]

One of the major developments in the field of artificial intelligence and computer science is advancement in machine learning. This allows a computer to adapt its algorithms as it knows more about the person using it[ CITATION Sim13 \l 1033 ] . This development has arguably furthered the gap between opposite poles in politics of today. For example, Valdis Kerbs has inquired the “suggested books” in Amazon.com when a user purchases a book and found out that in terms of books suggestions Amazon’s machine learning algorithm tries to find out about the person’s political orientation and recommends the books that will intensify his political opinion rather than diversifying it[ CITATION Val16 \l 1033 ]. Amazon.com’s aim behind the recommendations is most probably the likelihood of increasing the sales of books but the sheer ability of a computer to learn about a person and then manipulate his opinion by recommending books in itself a major change whose sociological and political ramifications need to be studied.

Similar recommendations are also made in Twitter, Youtube, Facebook and other social networking sites suggesting whom you should follow or befriend, or what videos you should see, based on the data that these companies have gathered about users. Since increase in website- activity is the major aim behind these recommendations, so it is understandable that they use content that users already know about and will be more likely to buy or click-on. These recommendations are embedded in the idea of homophilia and thus unlikely to create the opportunity for cross-political debate between people with different views. Twitter and

Facebook friends/follow suggestions are usually the people that are similar to the person who is being suggested.

Although twitter is a relative late comer among major social networking websites, but it has gained a special position due to its short and precise messages to followers. It is almost as if it’s built with celebrities, politicians and famous people in mind. Due to twitter’s architecture, it is possible to navigate through it and gather data about who is following whom. It is also interesting to observe how many people are receiving and actively participating (retweeting) in the conversations of people they are following. “Mentions” is also another way in which twitter is useful for gathering data about online political activity. All these ways of gathering data can be utilized to find out if twitter is actually a place for cross-political debate or is it a website, which instigates the polarization among people and further strengthen the clusters of groups that are formed on twitter. Itai Himelboim has argued that in twitter bird of feather tweet together and form a clusters of politically alienated groups[ CITATION Him13 \l 1033 ]. According to him, on major issues in US politics , twitteratess are divided in a way that they have grouped around some main hubs and formed an intense network where they just follow each other and hence the chances of them being able to have a cross-political conversations is quite minimalistic. There is a lot of literature existing on political activity on twitter but most of this stuff is related to the political issues pertaining to the first-world countries. For example the study mentioned above proposes a methodology that focuses on the issues that divide the American population on twitter and then this division is studied in detail. This methodology could be plausible for the first world countries like USA , which is a stable democracy with better internet and social- media penetration. However, for country like Pakistan where dynastic politics and charisma factor determines elections results more than issues it would more appropriate to use just the

‘follow’ relationship in twitter [ CITATION Sha13 \l 1033 ].

Twitter’s ‘follow’ relationship is one of the major directed relationships in social networks of today. It allows celebrities, politicians and other important people to be in contact with the people who are interested in their activities. One factor that plays major role in popularizing this

‘follow’ relationship is the credibility that twitter adds by verifying your account. Over time, the follow relationship has gained massive importance to the scale that now a significant amount of news worthy information is first disclosed through twitter accounts. In most cases these accounts have millions of followers who instantly, retweet, reply or mention the information to their own followers and thus the information is disseminated in an instant. Thus in essence, twitter is mostly about the who follows whom.

Twitter suggestions play major role in reminding users about whom to follow. According to twitter these suggestions are based on following criterion:

 “If you’ve uploaded your contacts to Twitter, we’ll suggest you connect with those who

already have Twitter accounts.  If someone has uploaded their contacts to Twitter, and your email address or phone number

is included in their contacts, we may suggest you follow them.

 Twitter may also make suggestions based on your location, e.g. the city or country you are

in.

 We may make suggestions based on your activity on Twitter, such as your Tweets, who

you follow, accounts you interact with, and Tweets you engage with.” [ CITATION Abo5 \l

1033 ]

Thus, the contacts a user already has in real life play a major role in determining who he will be in contact with him/her on twitter. As the last criterion suggests, twitter uses its history to suggest you new followers. This makes it reasonable to believe that instead of allowing a person to engage in a cross-political debate, there is possibility that twitter might actually reinforce the already existed network of contact around him and make it stronger. If a person is conservative,

As pointed from the research above , it is likely that he will be more interested in people similar to him and there is a good chance that after guessing that interest , twitter might suggest him similar people. In that way, this might lead to formation of clusters of interconnected people with similar interests. Thus, one might call this network formation as path-dependent. The more you engage with one school of thought in politics on twitter , the difficult it becomes to engage with other clusters. Thus in that situation it is important to ask the questions:

Do twitter users give an equal opportunity to political figures, to present their vision about issues? Is terms of ‘follow’ relationship on twitter, do users prefer to diversify the tweets they receive from different political entities?

These questions are actually a subset of a larger question, which asks if the factor of ‘choice’, which is unique to internet, has allowed the users to only listen to the people they already have a bias towards?.

For the purpose of this study, I will only be focusing on ‘follow’ relationship because it is clear and detectable indicator of the fact if a twitter user has allowed itself to be exposed to other twitter accounts or not.

Methodology review

For the purpose of this paper, we will use multi-methods to answer the questions. But all the data that has been collected and analyzed is directly from twitter and does not involve any direct interaction with twitter users. This way, the results would be reflection of actual behavior of users on twitter rather than how they perceive their actions (not that this perception data is not important).

There are two major tests involved in the process. During the first test, the aim is to find out the common twitter followers of the two real-life political competitors. It is recognized that the figure that comes as a result of this test is not really a perfect representation of the possibility of cross-political debate between political rivals .However, it is a reasonable beginning in a way that it gives us an estimate of twitter user who have allowed themselves to listen to both the political rivals on twitter and not become a part of a cluster around either of the rivals. For the test, stated above, it is necessary to gather the list of all the twitter followers of both the rivals. Because of extreme popularity of twitter with politicized people, it is common for a major political leader to have millions of followers on twitter. None of the tools available in the market allow this data to be collected as it is very time-consuming and twitter’s rate limits have made things worse for data collectors[ CITATION Twi \l 1033 ]. Hence, for the purpose of this article I employed Twitter’s APIs and used python scripts to gather the user-ids of the all the followers for every political rival. After compiling the list of user-ids, the data for both the rivals is then cross-matched to find the common followers of both the rivals.

The second test requires an analysis on a deeper level. The aim of this test is to answer the questions if clusterization is happening among the followers of political rivals. Here are the steps for the second test:

1. Gather the user-ids of the all the twitter followers of both rivals

2. Get a random sample of user-ids from all the followers of each rival separately.

3. Find out how many of the followers of one rival also follow the other rival and add these

accounts as nodes in the graph.

4. From the graph try to find out if clusters are forming around the political rivals

This methodology can effectively identify the state of rivalry between the political figures as it will clarify if their followers have allowed themselves to listen to the person they presume they don’t like in politics. As hypothesized above, there is good reason believe that the followers of the rivals have formed a cluster around one of these rivals and allowed themselves to only listen to him as they only follow the political supporters of this person. As stated in the justification for hypothesis, it is quite plausible to think that people have a high propensity to follow the people that are similar to them[ CITATION McP01 \l 1033 ].Therefore the inquiry we undertook was that if the users on twitter prefer to follow the people who all support each other and belong to a single political/social group in real life. With the 2-stage method stated above, it would be possible to do the cluster-analysis of the twitter followers of the selected political figure and to find out if they actually allow themselves to participate in cross-political debate going on twitter, or if they limit themselves to one sided opinions of the major figures in a political cluster.

Justification of Cases

For the purpose of this article, we have chosen two cases of political rivalries on twitter. The major reason for selecting these particular political rivalries on twitter is their diversity. They could not have been more different in any way possible. The first rivalry is taken from Pakistan, which happens to be a third world country with a history of political turbulence. Although

Pakistan is a democracy now but there is still a long way to go in order to achieve political stability. Majority of Pakistan’s population are people under the age of 25[ CITATION Wik \l 1033 ].

This is the segment of population that has had a reasonable exposure to new technologies and many of them have chosen to form accounts on social media websites. Twitter also has a approximately 2.5 million users in Pakistan.

In case of Pakistan, the rivalry that has been chosen is the official twitter account of Maryam

Nawaz and Imran Khan. Although she does not have any elected position in the political party,

Pakistan Muslim League Nawaz, but being the daughter of the leader of the party she posses a huge influence in the party and said to be the future of PMLN. She maintains an active presence on twitter, which allows her to be in contact with her millions of followers. Imran Khan on the other hand represents a new emerging political force in Pakistan, which represents the challenge to the existing political elite. He has successfully mobilized the young population of Pakistan and gained significant popularity in Pakistan to form a government in one of the provinces in

Pakistan. The rivalry between PMLN and Imran Khan’s PTI is well represented on twitter by

Imran Khan and Maryam Nawaz’s official twitter account. For the purpose of this paper we will run the above mentioned tests on this rivalry to find out if the cross political debate is happening among the followers of these two accounts.

The second rivalry that I have chosen for the purpose of this article is the Donald trump and

Hillary Clinton rivalry. As of date 15 May 2016, it is becoming increasingly clear that Donald

Trump is going to be the nominee for the Republican Party in general Election of USA and

Hillary Clinton will be nominee of the Democratic Party. Hence instead of targeting their party rivals both nominees have started targeting each other directly. As opposite to the political rivalry selected from Pakistan, this is a political rivalry from a first world country, which is going through an election. As it happens during the election period, people are actively making their choices about the person they are going to vote for and it is reflected on the twitter accounts too.

Both rivals are fast gaining many new followers every day. Another contrast between this political rivalry and Pakistani rivalry is that in this rivalry both of the rivals are actively perusing followers (as it is an election year) while in Pakistani rivalry both rivals have a relatively stable set of twitter followers. First Test

In analyzing, the political rivalries between Trump and Clinton on twitter while being focused on follow relationship is tricky in a way that both the twitter accounts keeps on rallying more and more followers every day. Hence, I limited my data gathering until 10th of May 2016. By this time, Donlad Trump has about 7.8 million followers on twitter and Hillary Clinton’s official account had about 6 million of followers. Following the first test, which includes the search for common friends, I found out that both of them have about 1 million followers as common.

Trump Hillary 1,029,854 6,783,372 5,036,573 As opposite to Donald Trump and Hillary Clinton the accounts of Pakistani political rivals are relitivly stable in terms of followers. The data for both Imran Khan and Maryam Nawaz were gathered on 12th of May. As of that date Maryam Nawaz’s official twitter account had about 1.8 million follwers where as Imran Khan’s official twitter account had about 3.6 million followers.As of the common followers are concerned , the interesting fact is that a huge majority of Maryam’s followers also follow Imran’s twitter account. Imran Khan

2,208,594 Maryam Nawaz

1,413,496 473,504

As it is visible from the result, there are some major differences between both the rivalries. One major contrast between them is the idea that there is a huge overlap of followers between

Maryam Nawaz and Imran khan and in contrast Hillary and Trump’s overlap is rather small when one compares with the number of followers they have. The possible reason for this difference in overlap could be that USA has a much larget population present on twitter and also offers a much wider variety of personalities to follow on twitter. In that scenario it was easier for an average american twitter user to have a greater choice when it comes to following people and he/could easily miss either of the political rivals such as donald trump and hilary clinton. However in Pakistan there is limited choice celebrities available for following and an average twitter user is bound to run into Maryam Nawaz or Imran Khan during their usage of twitter.

Another keen observation between the overlap of followers of Maryam Nawaz and Imran Khan is that almost 3/4th of Maryam’s Followers also follow Imran Khan, whereas the in case of Imran, a huge majority of his followers are exclusivly following his twitter account and not the accounts of his rival. A possible explanation could be that Imran specifically apeals to youth in his political campaigns and an average internet user in Pakistan belongs to that age-group and hence he has a bigger presence in twitter.

Whatever the explanation might be, one thing that we can be sure of from Hillary Clinton and

Trump graph is that about only 8 percent of the total population in twitter graph clinton and trump have allowed them to be exposed to tweets of both rivals. Whereas, in the Pakistani case the percentage of twitter user who have allowed themselves to be able to read tweets from both rivals stands at 35 percentage of total followers. However a big anamoly in these graphs is the huge overlap of Maryam Nawaz’s followers over Imran Khan, whereas a a rather large population from Imran’s followers remain unexposed to Maryam’s tweets. It is possible to infer from this fact that the possibility of cross-political debate between the followers of these two political rivals is uneven and favours Imran more than Maryam. The overlap between clinton and

Trump is relativly even as the difference between the number of their twitter followers is also relativly more even than the other rivalry. Thinking of both of these diagrams togather, brings us closer to answer of the broader question we are trying to inquire which examines if internet has allowed people to increase the awareness about all sides in a political debate?. While being focussed on twitter and perticularly on these two political rivalries, it is hards to reach a conclusive answer for the above mentioned question but the priliminary data suggests that majority of users on twitter prefer to follow either one of the two political rivals and not both. This might have to do with several factors but the scope of this paper would not allow us to get into that inquiry. The second step in the analysis of this rivalry will however elaborate more clearly what we have obsererved in our first test and in that test we will try to find out if the twitter user allow themselves to be a part of a political cluster formed around these politicians.

Second Test:

For the second test a sample size of 385 was taken from both trump and Hillary Clinton’s followers. The sample size was taken with the consideration in mind the level of confidence for the sample would be 95 percent with the margin of error for 5 percent. A graphic representation of this sample was drawn taking into consideration all the possible relationships among the nodes. At that moment the graph was a sampled down version of first test. Although all of the ordinary nodes followed either of the big (Hillary and Trump), but none of them had any interrelation among each other (None of the small nodes followed eachother).

After that, as described in the methodology section I took a look into their main supporters and tried to add them to graph. The aim behind this step is to see if there are clusters of people formed around the main nodes. This way it would be possible to find out the percentage of followers of either of the political rivals who also follow their main suppporters.Following has been set as criterion to find these personalities:

a. The personality/organization has officially endorsed either of the political rivals.

b. The personality/organization must have a significant following in the sample of

the either of the followers

c. The personality/organization must be actively involved in the debate between the

political rivals.

. In case of trump, using the criterion stated in the methodology folllowing set of people were fit the criterea.

1. Bobby Jindal

2. Chris Christie

3. Ben Carson

4. Marco Rubio

5. Sarah Palin

6. Scott Walker After adding these people to the graph, it look like this: As visible from the graph, the green nodes are much more towards Trump’s side then on hillary’s side which means that the people identified in the above list (Trump’s endorsers) have an obvious advantage among the trump followers than in Hillary’s followers. It is also noteworthy that all these green nodes are almost identical in size (which is determined by the number of connections within the graph).

In case of Hillary Clinton the few people who fit the above mentioned criterion are:

1. Bill Clinton

2. Elizabeth Form

3. Jerry Brown

4. New York Times

5. Chelsea Clinton

The result after these nodes were added to the graph(without including Donald Trump

Endorsments) look like this: Combing all the graphs above, the result turns out to be: In the graph it is visible that majority of Donald Trump’s followers (Green) are not following people from his network. There can be many plausible reasons for this, a major one could be that

Donald Trump’s celebrity status is much older than his inclusion in politics and that could lead to a set of followers who are not interested in politics and just want to follow a celebrity on twitter. In case of Hillary Clinton (Red Node), things are much different. A large number of people, who follow Hillary Clinton, also follow Bill Clinton, which is why Bill Clinton (2nd Biggest Red

Node) is one of the bigger nodes on Hillary’s side of the Graph. While its visible that trump’s green nodes are much more contained than Hillary’s red nodes, which are more spread-out. As a clear distinction between the previous Hillary-Trump graph it is visible that the red lines have much more penetration into green areas than vice-versa. This could mean that the people identified above as Hillary’s endorsements have better support among Trump Followers than

Trump’s endorsers have among Hillary’s followers. One possible factor that can affect the matter is that Hillary’s endorsements include One major news paper and a former president. Hence it was more likely that Hillary’s endorsements would have a significant following among the

Trump Followers. However, some Hillary endorsers have more followers among the Trump follower than from Hillary’s followers. These include:

1. Jerry Brown

2. Elizabeth Form

From the above 3 graphs it can be concluded that among Donald Trump’s twitter followers, a small chunk has allowed themselves to be exposed to Hillary Clinton and her endorsers and a majority remains connected only to Trump himself. Majority of them do not even have connections with Trump’s endorsers. Meanwhile in Hillary’s case this is not true. A majority of her followers also follow people among her endorsers, as visible from graph 4. About the Clusterization effect, it is visible from the diagrams that in case of both Hillary and

Trump only a minority of followers are being affected by it and the endorsers from both the camps have some following from the other camp too. However, it must be noted that it is much truer in case of Hillary Clinton than in case of Donald Trump. Her followers are having a higher subscription to her endorsers than in the case of Trump. The clusterization effect is much more visible in her case. However, the interrelations between the followers of the two political competitors are significant enough to establish that a cluster is forming around these politicians but clusterization is not as significant as proposed in the hypothesis.

Results of test 2 for Pakistani Competitors

As indicated from the results of test 1, there are many differences between the twitter dynamics of Pakistani political competitors than their contemporaries in USA.

As indicate from test 1, one of these differences is the number of common followers that they have among each other. In the first step of test 2, I have drawn a network diagram of test 1 using a smaller sample for both the competitors. As was the case of Hillary Clinton and Donald Trump, this sample has been chosen at random from the complete list of followers of both these politicians. The accepted margin of error was kept at 5 percent and confidence level of the sample was kept at 95 percent. The diagram turned out to be as following: As this graph clearly suggests Maryam Nawaz and Imran Khan have many followers in common among the sample size and as proved from the test 1, Imran has more independent followers than

Maryam Nawaz. The sample clearly reflects the facts discovered in test 1.

Moving on to the second step of test 2, I will add the important twitter accounts of Imran’s biggest supporter and try to analyze its impact on the graph. The selection criterion for these accounts is a bit different from selection criterion of Trump and Hillary Clinton since there is no election politics going on in Pakistan and official endorsement cannot serve as a criterion for selection of twitter accounts. For this case the criterion for selecting the twitter accounts will be:

1. Twitter account should have a significant following in comparison to the politician.

2. Account can belong to the party or individual who have undoubted allegiance to either of

the political rivals.

Keeping this criterion in mind, I have selected the following twitter accounts as a support network of Imran Khan:

1. PTI Official account (Party account)

2. Asad Umar

3. Jehangir Tareen

After these people have been added to the network above the diagram looks like this: As you can see the nodes have been sized according to the number of connections they have in the network. The nodes that have been added do have a majority of their connection with imran’s followers, but they do have a significant number of green nodes around them which means that they also have a big number of followers from maryam’s followers. This is an indication of the fact that followers of imran do tend to follow the people from his party and official twitter account of the party members but a significant number of people have preferred to follow Imran, Maryam and Imran’s party members. There is no clear clusterization happening in this case as predicted by the initial hypothesis.

Applying the same criterion as mentioned above to Maryam’s party PML-N, following twitter accounts served as important nodes for maryam Shareef .For this graph Imran’s important nodes have been removed, so that one can get a clear picture of clusterization effects among maryam’s supporters.

1. Shahbaz Sharif

2. Marvi Memon

3. PML N (Party account)

Adding these nodes to the first diagram of Imran and Maryam we get the following graph: As seen from the diagram, maryam’s main nodes are surrounded mostly by green nodes which is an indication that that majorty of followers of these nodes come from maryam’s set of followers or the common set of followers that both imran and maryam have. A very small number of

Imran’s independent followers have shown interest in following maryam’s important nodes. This does not however conclude that a significant cluster is forming around maryam’s node because of large number of common supporters among maryam and Imran. The picture can be more elaborate with important nodes of both politicians. From this diagram, it becomes much clearer that the common area between the two main nodes is huge and outweighs the independent areas (Green and Red) of both politicians. From here , it can be concluded that clear clusterization is not visible in case of Imran and Maryam and they have a huge chunk of common followers and most of the following of important nodes of both the politicians come from the these common followers. Conclusion

From results of the above-mentioned rivalries, it is seen that a clear clusterization is not found among either of the rivalries as inspected on this scale. However, most twitter followers in both the political rivalries have chosen to follow the supporting nodes of the political rivals which indicated that if this study is replicated on a bigger scale (bigger samples for test 2) it would be possible to get see more clearly whether clusterization effect is visible among the twitter followers of political rivals or not.