Kobi Omenaka Social Media Dissertation Practicality of Data Mining for Online Social Network Analysis

The Construction and Analysis of
Online Social Networks
Discourse into the use of data mining techniques for the construction and
analysis of online communities.
Kobi Omenaka
@00287065

Kobi Omenaka @00287065 2
List of Figures....................................................................................................................................................2
List of Tables......................................................................................................................................................3
Introduction........................................................................................................................................................4
Aims and Objectives .................................................................................................................................4
A Social Network........................................................................................................................................5
Social Network Theory and Social Network Analysis...............................................................9
Data Mining Evaluation and Research Methods........................................................................17
Extracting Data from Social Media Sources............................................................................18
Text mining using software such as Spinn3r....................................................................18
Programming........................................................................................................................................19
NodeXL.....................................................................................................................................................21
Netvizz......................................................................................................................................................26
Gephi..........................................................................................................................................................30
Method...........................................................................................................................................................33
Results and Analysis...................................................................................................................................34
Flickr Results.............................................................................................................................................34
YouTube Results......................................................................................................................................36
Twitter Results.........................................................................................................................................37
Facebook Results.....................................................................................................................................39
Shrinkage .....................................................................................................................................................42
Capoeira Vibe Case.................................................................................................................................43
Ethics....................................................................................................................................................................46
Quantitative vs. Qualitative ....................................................................................................................48
Conclusion.........................................................................................................................................................50
Appendix 1: Table to show the Number, Name, Group ID and Number of Nodes as
Extracted by Netvizz...................................................................................................................................51
Appendix 2: Python GitHub Repositories for Social Media Sites....................................62
Appendix 3: Other Web Resources....................................................................................................62
Appendix 3 Other Network Images...................................................................................................63
Bibliography....................................................................................................................................................65
List of Figures
Figure 1 Example of Network Diagram showing the Interactions in a Karate Club
(Easley & Kleinberg 2010)...........................................................................................................11
Figure 2 Network Diagram showing the Nodes and Edges on "ARPANET".............16
Figure 3 Showing the ARPANET network in Relation to its Real World Position in
the US........................................................................................................................................................17
Figure 4 Image showing screenshot of NodeXL starting page.........................................22
Figure 5 Image showing the NodeXL imports menu. Including the options to
report from Flickr, Twitter and YouTube...........................................................................23
Figure 6 Image showing the imports menu for a Twitter user network within
NodeXL.....................................................................................................................................................24
Figure 7. Screenshot of imported data from NodeXL from YouTube...........................25
Figure 8 Image showing the Netvizz User Interface within Facebook........................27
Figure 9 Image showing that the Netvizz data extraction has completed and can
be downloaded....................................................................................................................................28

Figure 10 Image Showing the Group Joining Process on Facebook..............................29
Figure 11 Network graph produced in Gephi showing a Manchester based
capoeira group.....................................................................................................................................31
Figure 12 Network Graph Showing the Keywords Associated with the Term
"capoeira" on Flickr..........................................................................................................................34
Figure 13 Network Graph created from the "capoeira" tag within YouTube..........36
Figure 14 The Most Frequent Posters of Capoeira Videos on YouTube.....................37
Figure 15 Twitter network produced from the hashtag “capoeira” (from 100
tweets)......................................................................................................................................................37
Figure 16 Twitter network produced from the hashtag “capoeira” (from 1000
tweets)......................................................................................................................................................38
Figure 17 Network Graph showing 75453 nodes constructed from 241 Capoeira
Groups......................................................................................................................................................39
Figure 18 Image to show the Top 10 most Connected Nodes from the
Constructed Capoeira Network.................................................................................................40
Figure 19 Network Graph Showing of the "Capoeira Vibe" Network Community
on Flickr...................................................................................................................................................43
Figure 20 Capoeira Vibe level 1.0 YouTube Network............................................................44
Figure 21 Capoeira Vibe Level 2.0 YouTube Network...........................................................45
Figure 22 Capoeira Vibe Twitter Network...................................................................................46
Figure 23 Social Technographics Ladder based on U.S. Adults (Li & Bernoff 2011,
p.43)...........................................................................................................................................................49
Figure 24 Network Graph of 241 Capoeira Facebook Groups with Labels ..............63
Figure 25 Capoeira Vibe Level 1.5 Network on YouTube....................................................64
Figure 26 Image showing the Giant Component from the Facebook capoeira
network. In spite of the different colours depicting different groups each
node can be connected to each other. The longest distance between any two
nodes is 22. This contains 68448 nodes compared to 75453 in the complete
network....................................................................................................................................................65
List of Tables
Table 1 Key Values from Netvizz Data Extraction of Facebook Capoeira Groups41

Introduction
“The web is more a social creation than a technical one. I designed it for social
effect - to help people work together - and not as a technical toy. The ultimate
goal of the web is to support and improve our weblike existence of the world. We
clump into families, associations, and companies. We develop trust across the
miles and distrusts around the corner” (Berners-Lee 1999).
This quote from Sir Tim Berners-Lee, the inventor of the World Wide Web, gives
a poignant insight into the direction of the research that will be summarised in
the following pages.
The intention of this research is to be an independent introduction into the
world of networks, of on and offline including key theory concepts. This
progresses to assessing methods that can be used to construct these networks
from online resources via data mining. This is presented in the form of research
based both on theoretical and real-world networks.
Once the networks have been established the key questions then follow:
 How can this information be used?
 Are there any ethical concerns related to data mining?
These are discussed towards the end of this report followed by an overall
conclusion.
Aims and Objectives
The research the aims and objectives of this project are highlighted below:
1. Introduce basic social network analysis (SNA) and networking theory.
2. Evaluate the use of various data mining techniques to establish social
network. This includes the use of programming as well as the more
accessible and freely available forms of SNA software.

3. Use the keyword “capoeira” to construct a social network based on the
data mining techniques discussed and use network analysis theory to
evaluate them.
4. Confirm whether a giant component exists within the capoeira network
5. Establish a new concept “shrinkage” within the network of capoeira on
SNSs.
6. Discourse into the ethicalities of these techniques
7. Evaluation of how valuable the results are without a qualitative aspect.
8. Case study: Evaluation of research techniques with online capoeira
community “Capoeira Vibe” that has accounts on Facebook, Twitter, Flickr
and YouTube.
By nature human beings are social creatures. Any one individual may form part
of any number of networks and groups connected by family, school, university
friends, teammates and colleagues ties etc. An individual’s role within each group
may vary as will the strength of the ties.
It is interesting to see Berners-Lee’s vision the Internet being one of social
concern rather than technical and being designed, in a sense, to magnify the
social experiences that preceded it. The premise of this research is to investigate
social networks on the Internet.
In order to do this we need to understand networking theory, which is based on
anthropological and socio geographical research.
A Social Network
A social network is a social arrangement made up of “nodes”, which are typically
one person that interact with other nodes. The interactions between each node
such as family ties, friendship, academic links, business connections etc. are as
“edges” and structure particular networks.

On a basic level any network can be defined by the number of nodes and by the
edges that occurs within it.
The concept of a social network is not a new one but is one that has come to
particular prominence in the last decade with the advent of Web 2.0 and
communications made possible via social networking sites (SNS) on the internet.
Of course social networks predate the Internet. By its very nature the internet is
perhaps the world’s largest social network and was, in fact created as a “network
of networks” (Kurose & Ross 2003, p.2) that linked educational facilities
together.
Using the Internet as an oversimplified example of a social network, each
computer could be seen as a node, whilst connections to the other computers can
fulfil the analogy as the edges.
The Internet has facilitated the formation of connections and, to some extent the
growth of networks. This is because “networks are now freed from the
restriction of a geographical location”(Easley & Kleinberg 2010, p.1).
As people are becoming more web literate and webs services improve the easier
it becomes for like-minded people and institutions across the world to find each
other. Those people that were previously isolated can now find it easier to join
networks.
“What makes social network sites unique is not that they allow
individuals to meet strangers, but rather that they enable users to
articulate and make visible their social networks. This can result in
connections between individuals that would not otherwise be
made,”(boyd & Ellison 2007).
It is difficult to determine how the Internet has affected the number of nodes
within a particular network. What can be seen, however, are the virtual
connections made to real-world networks. An example of this would be the

association with brands such as Coca-Cola. The advent of SNS’s doesn't
necessarily mean there more Coca-Cola drinkers. They can, however, show their
affiliation easily by joining groups or “liking” Coca-Cola’s page on Facebook for
example.
The SNSs however aren't only restricted to commercial brands “one finds
networks appearing in discussion and commentary on an enormous range of
topics”(Easley & Kleinberg 2010, p.1). At the time writing Coca-Cola’s Facebook
page is at number 10 based on the number of “likes” and is the highest placed
tangible product in the list. Also in the top 10 are musicians, sports stars, TV
shows and also the brands Facebook and YouTube. Unsurprisingly, Facebook
comes number one in the list of Facebook Fan pages (Fanpagelist.com 2012). All
of these have something to sell and benefit directly from having contact with
their fans on Facebook and Twitter et al. SNS’s can be described as
“web-based services that allow individuals to construct a public or semi-
public profiles, articulate a list of other users with whom they share a
connection, and view and traverse their list of connections and those
made by others within the system” (boyd & Ellison 2007).
It’s also easy to support causes such as Greenpeace or Oxfam using “likes” or
“tags” on SNS’s. Whilst these global names take up the lion’s share of attention,
smaller groups and fan pages from community centres, small and start up
businesses, local charities and amateur sports teams etc. should not be ignored.
We can see more and more groups of all shapes and sizes seeing the benefits of
integrating SNS’s. A common example of this would be to see how SNS’s are
linked on their websites. It is becoming more and more important for small
groups to become involved with the activities on SNS’s and be familiar with their
social networks.
The importance of SNSs and networking lies in the fact that any party involved
can initiate dialogues easily. It is this two-way interaction that differentiates
social media from traditional broadcast media such as TV, newspapers and radio.

“individualized messages can simultaneously be delivered to an infinite
number of people, and, each of the people involved shares reciprocal
control over that content.” (Crosbie 2002).
This presents opportunities to exploit the new medium. Both profit and non-
profit organisations can benefit from being contact with their potential
customers and fan base. Social media is about “reaching and connecting people”
(Trompeter 2010). The fact that it is a low cost way to reach certain audiences
means that non-profits and commercial groups alike can start to experiment
without too much financial investment. The use of “likes”, keywords and tags and
user forums can make it easier to identify target markets and people who might
be interested supporting causes.
The problem with social media is that it is quite labour-intensive. In a small
organisation the people who will be using social media to connect with people
may not have the time or the ability to make best use of the medium. For this
reason the wisest use of social media would be to highlight the potential
audience before initiating the conversation. This approach will be more cost and
time effective and as a result of better rewards will be reaped. The scattergun
approach of traditional media has now, in a sense, been refined to pinpointing.
This, however, is easier said than done. With 6.5 billion people on the planet how
do you pinpoint a target audience? Do some people in the target audience have
more effect or influence than others?
What about the unidentified people who do use your products that don’t visibly
like them on the Internet? According to Comscore’s white paper the “Power of
Like” Friends of Fans on Facebook typically represent a much larger set of
consumers, 34 times larger, on average, for the 100 brands (Lipsman et al. 2012,
p.2).
These questions might be difficult answer but are important to consider. There
are clearly economic gains to be made from utilising social media and social

networks. “Simple online economics: on the Internet, traffic equals money”(Li &
Bernoff 2011, p.11).
This quote taken from Li & Bernoff’s book “groundswell” could be a strapline
advocating the use of social media for promotion. In this rapidly changing
technological environment establishing and understanding online social
identities could prove vital for success. It is important to note that as well as
economic gain, these techniques will also apply to academic and socio-economic
research.
This research will address some of the questions highlighted above by looking
further and deeper into how online social networks can be identified and
constructed. Once constructed the networks can be analysed using SNA
techniques. The purpose of the analysis is to try and determine the size or
extents of the networks created and then establish the key people and
relationships i.e. “nodes and edges”. The argument is that knowledge and
understanding these network qualities can be a key factor in realising
organisational goals.
By not restricting research to a local area means that and network can be
constructed without having to filter for location. This means, however, that it
might be more difficult to construct a network that is truly representative. In
order to establish meaningful and true relationships on a global level the amount
of data collected must be significantly higher, when compared to a local area
such as a town or city. This calls extremely robust and accurate data collection
techniques.
Social Network Theory and Social Network Analysis
Social network analysis (SNA) focuses on the study of patterns of connection in a
wide range of physical and social phenomena (Hansen et al. 2010). It is argued

that analysis of networks is more concerned about the social interactions rather
than individual qualities of the nodes. Traditional social study emphasised the
qualities of the individual, whereas SNA takes an alternative viewpoint.
Individuals are seen as being less important than their actual relationships with
other nodes in the network.
Research has explored everything from foundational physical systems created by
chemical and genetic connections, animal food pyramids and distinctly human
characteristics such as social cohesion, privacy, markets and trust (Hansen et al.
2010). Social networking analysis (SNA) has been used in the field of disease
research helping to understand how patterns of human contacts help or hinder
the spread of contagious diseases in a population. SNA techniques have also been
used as a mass surveillance, helping determine whether individuals or any
member of a population is a potential terror threat.
The above shows that SNA uses are varied and important. SNA has a history
dating back to 1954 when social anthropologist J.A.Barnes first coined the term
“Social Network” in his journal article “Class and committees in a Norwegian
island parish”.
The early pioneers of SNA placed much emphasis on the gathering of qualitative
and quantitative data by surveys and interviewing techniques. The quantitative
data is has been described above that focuses on the relational ties within any
particular social network.
Qualitative approaches use interviews and open survey questions to generate
data to construct social networks. It is concerned more about how those ties and
relationship bonds were formed within a network and the experience with it.
This kind of research comes from a more anthropological viewpoint and often
focuses on personal networks including communities, neighbourhoods and
friendship groups. As a result of the data gathering techniques used the sample

sizes are usually a lot smaller than with quantitative analysis, or may run over
extended periods of time.
Quantitative methods may involve the collection of vast amounts of data before
mathematical equations and algorithms are applied to it. This data can then be
processed using software or spreadsheets such as Microsoft Excel networking
diagrams.
This research will focus primarily on the methods for collection and organisation
of quantitative SNA data. It will also compare and contrast qualitative versus
quantitative data as a means of determining contextual usage.
Much of recent SNA has been focused on the visualisation of these social
networks, such as Figure 1 below, and applying graph theory to analyse it. It is
the use of graph theory that can highlight certain relationships within networks.
Figure1 Example of Network Diagram showingthe Interactions in a Karate Club (Easley& Kleinberg
2010)
It is worth talking about graph theory before moving to the next sections of this
research. This will better explain the results produced from the different
methods.

In the network above the nodes 1 to 34 are aggregated into cliques that are
connected to one another by relations. This positional approach focuses on the
pattern of relations in which individuals are involved. Connections between
other nodes in the network defines its position. (Burt 1978). In the karate club
shown in figure 1 it can be seen that nodes 1 and 34 represent two key nodes
within the relationship of the club, but they are not connected each other.
In graph theory nodes 1 and 34 are not neighbouring nodes as they are not
connected by a single edge. They are, however, connected by a “path” which is
simply a chain of nodes. A path from node 1 to 34 can be defined as 1 to 14 to 34.
Longer paths can be defined, however, often the average shortest path between
all nodes is defined as a quality of the network. A path is defined by the number
of edges in between the two nodes in question. This means the first part as
described above has a length of two(Jackson 2008, p.25). Consider how this is
important with respect to the spread of contagious diseases.
Another quality of networks is how well “connected” it is. A graph is referred to
as connected if there is an edge between every pair of nodes in the social
network. This essentially means that there are no floating sets of nodes and
everything is linked together as one mass “component”. Constructed networks
aspire to exist as connected networks so that dataflow to all nodes is possible if
not easy. There is no reason, however, to suggest all networks especially social
networks should exist as completely connected ones. It is common to process
networking data to find that the separate distinct components within the data
sets. A graph can then be separated into its components as one of it’s
characteristics before going further to describe the characteristics of each
component separately. Figure 1 shows an example of a fully connected network.
In networks that are constructed from large data sets it is common to find sets of
distinct components with one large one in particular that is referred to as a
“giant component” (Erdös & Rényi 1960, p.55).

To consider how this might work a thought experiment can be performed.
Consider a network made up of all the individuals in a university including the
undergraduate students, graduate students, academic staff, administration staff
and facility staff etc. An edge is formed between people who know each other by
name. When considering this network it is conceivable to think of many groups
of people i.e. components of which it is impossible to link them together via a
path. This is likely to exist even if this network is constructed at the end of the
academic year. It is conceivable, for example, that the caretaking staff in the
science faculties could be disjointed from the admin staff in the geography
department. Hence the creation of distinct component parts.
The existence of a “giant component” is relatively easy to explain as only the only
requirement is for two large components to be joined by one just edge. In the
case of universities where students, as they live together, study together, play
sports together, socialise together etc. would form a large components in
themselves. The students contacts with the lecturing staff would bring academic
staff and their faculties into this component. The academic staff of course have
contact with the administration staff who are then brought into this component.
It becomes difficult to see who wouldn’t be part of this giant component.
One common attribute of such “giant networks” is how small the paths are from
one node to another. This is referred to as “The Small World Phenomenon”
(Milgram 1967). In an experiment, conducted by Stanley Milgram in mid 1960’s,
he tested the theory that any two people can be linked by short paths. This is a
good early example of data collection in order to produce networking data. At
the time the experiment they had a limited budget of $680(a little over £3000
inflation adjusted for today) and no access to any of the networking data that
they would have today. The experiment ran by giving 296 randomly chosen
people a letter that had to be forwarded to target person. Each of the 296 people
were given some information about the target including his address and
occupation. They were asked to give the letter to someone and they knew
personally with the same instructions to forward on to the next person. In terms
of graph theory each person was a node and each “forwarding of the letter”

referred to as an edge, forming a path from the start person to the target. Of the
296 starting letters 64 successfully made it to the target. Of the 64 that made it
the average path length was six. This experiment is the source of the well-known
phrase 6 degrees of separation. There are many problems with this experiment
and it’s announced results, the first being that 232 letters did not reach the
target. In addition this experiment was conducted in a small area around Boston
in the United States. It is certainly not conclusive evidence that there is only 6
degrees of separation between any two people on the planet.
Whilst Milgram’s experiment was not conclusive evidence of the small world
phenomenon this is something that has been experimented on numerous ways
over the years. When large data sets exhibits a giant component it is often found
that the number of steps between each node is small. Lescovek and Horvitz
conducted a more recent experiment, published in 2007, with a larger dataset
using Instant Messenger. After analysing the accounts of 240 million active
Instant Messenger users they found a giant component consisting of most of the
nodes and that giant component had mean average path length of 6.6(Leskovec
& Horvitz 2008) and a median of 7. Close to that found by Milgram in the 1960’s.
The Instant Messenger experiment is an example of the type of research on
large-scale networks that has become available in light of access to large data
sets on the Internet. These kind of data sets were difficult to analyse previously
and with the scales involved is extremely difficult now to process without
computer power.
Capoeira as the Basis for Networking Research
In order to add value to a particular type of research it is worth considering the
distinct reasons why a particular data sets might be studied. (Easley & Kleinberg
2010)
1. One may care about the subject matter in question.
2. The data sets is being used are related networks that might be difficult to
measure.

3. There is a search for properties that appear to be common across many
different subjects.
This research is focused on the Brazilian martial art of capoeira. Capoeira is a
martial arts from Brazil that combines elements of dance, acrobatics, fighting and
music. Capoeira has spread all over the world with many Brazilian instructors
teaching in countries ranging from all the countries in the Americas and in all the
continents. In addition it is common for capoeira practitioners to travel outside
of the home country to attend events. The spread of capoeira in real terms is
large and the contacts that “capoeiristas” (practitioners of capoeira) have with
each other is frequent, varied and strong enough to form links on SNS’s such as
Facebook(Essien 2008; Green & Svinth 2010). The spread of capoeira itself has
been in large part due to the advent of social media and in particular the sharing
of videos on sites such as YouTube. The research into the structure of capoeira
should be an interesting case study giving insight into the structure of martial
arts, dance, music and national communities and clubs as well as emerging
activities such as free running and parkour.
In relation to the three points noted above:
1. The subject matter of capoeira is a key interest to the researcher
2. The results from this can be related to other emerging past times and
hobbies.
3. It is of interest to see how attributes such as giant components and small
world phenomenon manifest itself within this network.
From a research point of view, the word “capoeira” as a keyword is a strong one.
The affiliation with the word “capoeira” is strong and is likely that only people
with more than a passing interest, i.e. practitioners, will search for it let alone
affiliate themselves with it as part of network. Capoeira is a single word that can
be tricky to spell, but those that know it are unlikely to spell it incorrectly. It
would be hard to accidentally type “capoeira” as a keyword. In spite of each
social media service being different, the word “capoeira” should highlight
interested participants across all of them. This will identify social networks in

each social media service(Jones 2011). One key point worth noticing is that
capoeira is a singular term so the amount of variables involved in searching for
this keyword is limited. This should vastly reduce the amount of irrelevance,
inconsequential data that is collected.
It is interesting to conclude this section by considering the earliest example of an
online network i.e. the Internet. The Internet was first started in 1970 as
ARPANET which was a network of 13 U.S.-based universities. With each
university considered a node then the networking diagram can be drawn as
shown below in figures 2 and 3.
Figure2 Network Diagram showingthe Nodes and Edges on"ARPANET"
Figure 2 shows a networking diagram that we could we now know to be
connected as all nodes are connected by image to one or more of the nodes. This
was the intention when ARPANET was first developed to ensure that in the event
of one edge going down, information contained in one node would still be
accessible.

Figure3 Showing the ARPANET network in Relation to its Real World Positioninthe US
Figure 3 above shows the same ARPANET network but set against its real
location on the US map. This helps to provide context to the networking diagram
as shown in figure 2. If we viewed the Internet as it stands today it’ll be
extremely difficult to navigate the networking diagram without the aid of
computer software. The Internet has grown from its early humble beginnings
with 13 just in the US to an estimated 361 million nodes at the end of 2011
worldwide. It’d be interesting to see just how large the giant component would
be in this network.
Data Mining Evaluation and Research Methods
This research was inspired by the discovery of research advocating the use of
data mining in order to extract information from online sources. Data mining can
be defined as:
“a business process for exploring large amounts of data discover meaningful
patterns and rules”(Linoff & Berry 2011)

The term “business process” within this definition perhaps makes data mining
sound “cold-hearted”, however, all organisations, profit making or otherwise, are
involved in business activities. Data mining, if performed well, will help increase
profit margins as well as help increase donations, active participation and
support to charities, and improve academic research.
The Internet, and SNSs in particular fits the definition of “large amounts of data”
as given above. The problem is not the amount, rather the filtering of data to
produce meaningful results.
Extracting Data from Social Media Sources
A number of methods were considered for the data mining parts of this research.
The initial ideas included the use of search engines to look for trends and data,
combinations of proprietary software through to tailor-made coding using freely
available programming languages. Each method has different levels of usability
that relates to the time and cost investment required to produce usable results.
It was perceived that the ability to code and tie in with SNSs API’s and databases
would produce the most meaningful results. An API, Application Programming
Interface, is the hidden side of many large net websites and programs and allows
users with additional knowledge access to data.
A breakdown of the techniques evaluated is given below.
Text mining using software such as Spinn3r.
As discussed earlier SNA can be useful for tracking the spread of contagious and
infectious diseases. A recent saw tracking of the word “influenza” blogs (Corley
et al. 2010) using a web service called Spinn3r (http://spinn3r.com/) to crawl
and index content

“text mining has proven to be more difficult than data mining, as the source data
consists of unstructured collections of documents rather than structured
databases” (Corley et al. 2010, p.600).
Spinn3r is limited to searching and indexing blogs and cannot gather information
from SNS’s. Text mining software does have its merits and can be used as a key
indicator to track trends with in particular subjects over time. The time required
to produce results that can be used to fix patterns is prohibitive over the course
of this research.
It was decided at the preliminary stages not to use text mining as a source of
information for this research.
Programming
Resources such as “Mining the Social Web” (Russell 2011) propose that with
very little programming ability it is possible to extract data from Facebook
Twitter and LinkedIn. This concept is interesting and provided much of the basis
for this research.
Mining the Social Web advocates the use of programming language Python for
making the code the data extraction. It jumps quickly to the use of example
Python code as a means of introducing the subject matter. As the programming
ability prior to commencing this research was limited a decision was made early
on in conception that a foundation level should be established. The intention was
that any data mining code used should be understood to the level that
modifications could be made.
In the months before this research was started a basic level of programming
knowledge was acquired with a combination of self-learning books such as
“Hello World” (W. Sande & C. Sande 2009) and established, professional level
online training resources provided such as “Foundations of Programming:
Fundamentals”(Allardice 2011). These helped to establish a good level of

understanding of programming such as the syntax, methodology and coding
strategy.
After completing these courses and books progress went then went to specific
coding for data mining in Python.
There are various sources of code for data mining in Python, which can be used
as the basis for research. Books are an important start and help get a good
grounding but can quickly become out-dated especially when trying to connect
with fast-paced technology such as the Internet. SNS’s continually update their
API’s meaning that programs that want to interact with them have to update.
One way of doing this is by using social coding repositories such as “GitHub”.
GitHub provides a place for programmers to leave their code, which can be
updated and modified by other uses as they see fit. This is higher level of
transparency aims to provide the highest quality of code, from a number of
different sources, that can constantly evolve. In such places code is constantly
changing scrutinising and involving the better(Dabbish et al. 2012).
This research investigated a number of Python GitHub resources as shown in the
Appendix 2 at the end. This includes GitHub resource related to the book mining
the social web (https://github.com/ptwobrussell/Mining-the-Social-Web).
“Mining the Social Web” was published in January 2011. When the book was
published it contains details of how to data mine “Google Buzz” which was
Google’s SNS’s at the time. Google Buzz has now, of course been replaced by
Google+ and provides a good clear example of how technologies change quickly
in this industry.
Progress with this route was initially very promising and the understanding of
programing after the foundation and self-training period seemed strong. Despite,
however, having a good understanding of how programming syntax should
work, a lack of experience exposed the weakness in this method of approach.
There were different approaches used to try and readjust the research and
maintain progress in order that results from this method of data mining gained.

Whilst proceeding along this route of investigation problems were experienced
in the following ways:
1. Modifying code in order to interact with API’s
2. Lack of understanding of the social media API’s
3. Online user forums could not clarify problems
4. Suggested methods of visualisation incompatible with computing
platform
During this period progress was slow and discouraging. It became clear that
despite understanding how the programming code should work, the lack of
experience with programming and using API nomenclature would set too many
roadblocks for a project of this scale.
The decision had to be made, not to continue with using Python programming to
mine data, in order to continue to try and achieve some of the objectives
highlighted.
NodeXL
The focus turned to other ways to explore and mine online. It was accepted that
other methods might not be able to produce specific results, however the
stability and reliability that they could provide was vital at this stage.
NodeXL is a free downloadable template that integrates with Microsoft Excel. Its
advantages compared to core programming are numerous.
1. The interface is a template within Microsoft Excel so those familiar with
spreadsheets should be able to use it.
2. Due to its integration with another program it is very stable to use.
3. Built in functionality with the Twitter, YouTube and Flickr APIs.
4. NodeXL is open source software which means that anyone with
programming knowledge has the ability to make additional add
additional functionality to the software. This should keep template up-to-
date with respect to the APIs but also promises more for the future.

5. The data collected within NodeXL can be exported to other the
visualisation programs.
Once installed the NodeXL interface is understandable and are intuitive. The
NodeXL templates exist completely as a tab within the newer versions of
Microsoft Excel. Clicking the NodeXL tab presents a dedicated “ribbon” complete
with required icons and menus. See Figure 4 below.
Figure4 Image showingscreenshotof NodeXL starting page.
The ribbon is dominated by the import and export icons that lead you to further
menus for importing mined data. See figure 5 below, showing the import menu
which includes importing options and several data mining options for Flickr,
Twitter and YouTube as discussed previously. Of the SNSs searches can be made
based on a particular users network and on a particular keyword.
With the ability to search a particular user it was decided to perform a case study
on the user name “Capoeira Vibe” that exists on Twitter, Flickr, YouTube and
Facebook. The intention is to compare and contrast the networks of this one user
across the four main social media networking platforms.

Figure 5 Image showing the NodeXL imports menu. Including the options to report from Flickr,
Twitter and YouTube
The method for extracting data from YouTube, Flickr and Twitter sites is similar.
Each is a case of entering the username or keyword of the network that you wish
to investigate, the kind of relationships want reporting and a limit to the number
of nodes that to inspect. See figure 6 below. One difference is that Twitter
imposes a data retrieval rate of 350 per hour (Hansen et al. 2010, p.152). This
sounds like a considerable number but is quickly reached if the network is
anything other than small.
It should be noted that this restriction is not to limit the absolute amount of data
collected from Twitter it is more to restrict the bandwidth as the amount of calls
a data for the service is very high. This can have a considerable time impact on
the collection of data, with larger networks taking several days to complete
during the course of this research. Planning required if large twitter networks
are to be investigated.

Figure6 Image showingthe imports menu for a Twitter user network within NodeXL
It can be seen in Figure 6 above that there are various options for investigating
each SNS. This shows an example of an import based on Twitter username. The
options selected determine the extent of the network, where nodes (referred to
as vertices here) can be created just for the follower, the person being followed
or both. How the edges are defined during the data gathering can also be
determined at this stage. Edges can be added in Twitter for a “following”
relationship as well as responses to tweets between people that don’t necessarily
follow each other.
The final key point to choose is how “deep” the network should go. A network of
level 1.0 looks at the user and links to their followers and tweeter relationships.
A network of level 1.5 establishes links between the followers and back to the
user under investigation. Taking a step further to a network level of 2.0 looks to
the network of the followers of followers.
The amount of information requested at this stage determined by the number of
edges vertices or the level of network has a direct impact on the amount of data
requested by NodeXL of each social network.

This is worth noting for two key reasons.
1. The amount of data requested has an effect on the processing required
once collected. The data requested should relate to the requirement as
the moderator takes more processing power but also more power to
analyse once gathered.
2. The more information requested has an impact on the time frame for
collection. This is particularly relevant to the limitations imposed by
Twitter as discussed above.
The raw output from NodeXL is a list of the nodes and the relationships between
them, but can also include items such as thumbnails tweets and hyperlinks.
Figure 7 below shows a typical screen that is presented after an import has been
completed. This depicts an important after a keyword search on YouTube for the
term “capoeira”. Presented on the right hand side is a raw version of the network
graph before any manipulation.
Figure7. Screenshot of imported data from NodeXL from YouTube
NodeXL proves extremely useful for the extraction of data but less useful when
analysing the networks produced. One major problem is that during data
extraction the visualisation of existing data is not possible. This limits the
production and processing data to a "one way" system, impeding progress.

Another problem was that the graphical visualisation and network static power
of NodeXL did not match other software such as Gephi. It was decided to export
the files created within NodeXL and manipulate them within Gephi.
Netvizz
The main omission in NodeXL’s data mining arsenal is the fact that it does not
currently link in with Facebook. Whilst Facebook is only one of many SNS’s
available it is clearly the most well known and used. During the course of this
research the number of monthly active Facebook users surpassed 1 Billion (1
000 000 000) users (Zuckerberg 2012). Its impact on the social media landscape
cannot be ignored.
As such this analysis would have felt incomplete without endeavouring to use
data from Facebook as a source for network construction.
Research into extracting data from Facebook led to “Netvizz” which is a small
Facebook app. There is little formal information about Netvizz other than it is a
program created and maintained by Professor Bernhard Rieder at the University
of Amsterdam (Rieder 2012). The application can be accessed by typing
“Netvizz” into the search box within Facebook.
Once Netvizz is accessed a screen such as the one below is presented.

Figure8 Image showingthe Netvizz User Interface within Facebook
This page shows that Netvizz can be used to extract data from users personal
networks as well as Facebook groups they are members of. Data from Facebook
brand pages cannot currently be extracted.
Clicking on a group name then opens a page showing that the extraction
algorithm is running and tracks progress. A typical page is shown in the image
below.

Figure9 Image showingthat the Netvizz data extraction has completed and can be downloaded.
In order to fulfil the aims and objectives set up earlier in this research it was
decided that the data from a number of clubs should be extracted and
amalgamated.
Users can only extract data for Facebook groups that they are members of so in
order to complete this research a number of capoeira Facebook groups had to be
joined. These groups were simply found by typing the word “capoeira” into
Facebook search box and requesting to join. A request to join all groups
presented by this simple search was seen as the best way to keep this research
impartial and random. The more random the sample taken, greater degree of
accuracy and confidence can be had in the results (Kotrlik & Higgins 2001). It is
anticipated, however, that as Facebook presents news and information to a
particular user based on their friends, that is similar presentation will occur
when searching for groups. This is governed by Facebook’s “Edgerank”
Algorithm (Bucher 2012), which seems to limit sources and visibility of
information received rather than opening up social media landscape in a

panoptic fashion (Foucault 1977). This leads to the real concern that achieving
randomness online and in Facebook in particular is difficult. This has led to the
introduction of the phrase Filter Bubble, which relates to how the algorithms and
software that govern the Internet actually serve to hide information from by
aiming to provide users with information that is being most relevant to them
(Pariser 2012).
Figure10 Image Showingthe Group Joining Process onFacebook
The image above gives a typical example of a search page produced after typing
word capoeira into Facebook. There are three distinct stages for the displayed.
1. The groups that have been found by Facebook search and have yet to
have a join request sent
2. Those where the join request is still pending approval by a group
administrator
3. Groups that a user is a member of
It should be noted that there are two distinct types of groups on Facebook, open
groups and closed groups. Both require approval to join so it was felt requested
to join a close group would be the same ethically as requested to join an open
group as admission depends on the administrators within the group.
Once a join request was sent it could take anywhere between one minute and
three months for an approval to be given. Indeed, many club admins approve the
joint request during the time this research was being performed.

The request to join was sent to over 300 capoeira clubs. The number of clubs
joined was due to be limited to the research period time constraints. It was,
however, found that Facebook started to restrict the number of groups that was
shown with each search. This started to occur once the number of join requests
was around 200 in a relatively short space of time, approximately one week.
This is another indication that the group selection process was not performed as
randomly as possible.
Once the request to join portion had finished the next stage was to use Netvizz to
extract data from each group. Once the extraction is completed the data can be
downloaded as a .gdf file for use within Gephi for analysis.
The extraction time can vary mainly depending on the number of users within a
Facebook group. In some cases the extraction fails. This is something that has to
be accounted for and accommodated for when undertaking analysis of large
groups. It is not a labour-intensive process, however, time is required to monitor
the groups and should be considered for similar undertakings.
Gephi
Gephi is an open source network manipulation software. It uses algorithms and
tools more prevalent in the gaming industry to be able to filter visualise and
exports networks of all sizes. Gephi uses the computers graphics card to do
processing, which means that other operations can occur whilst graphs are
calculated. This does mean, however, that a more powerful graphics orientated
computer is beneficial for optimal usage (Bastian et al. 2009).
Gephi does not have the ability to import the data itself. It can, however, import
a number of different types of files including files type generated by NodeXL and
Netvizz as discussed above.

The Gephi interface, graphs produced and outputs such as path length mean that
meaningful analysis can be performed once a network has been loaded in. As
graph theory algorithms are built-in to Gephi there is as short learning curve for
those that are well versed in network science. The graph theory and algorithms
were built in from the ground up along with the visualisation aspects of Gephi.
Those unfamiliar to networking analysis but shown a Gephi network graph
should be able to understand key characteristics. Figure 11 below shows a
network graph produced in Gephi.
Figure11 Network graph produced inGephi showinga Manchester based capoeira group
The graph above as shown in figure 11 is produced from a capoeira group
“Capoeira Cordão de Ouro North West UK (Manchester and Liverpool)”as
identified in Facebook. This network graph is a slightly cropped down version of
the group with the unconnected nodes (leaves) removed.
Based on the import of the data into Gephi there are 1001 nodes in capoeira
group. Once the unconnected nodes are removed there are 879 (87.8%) visible.

By looking at the graph, with the leaves removed, it can be seen that can there is
one large connected network component here.
It is clear to see that C.M.Parente is the most connected node based on the size of
the node showing and the text presented. It should come to no surprise that
C.M.Parente is the instructor of this capoeira group.
This capoeira group is extremely well connected, with an average path length of
2.43. This means that there is less than 3 degrees of separation between anyone
in this group. This is well below the 6.6 as discovered by the research on instant
messenger platform MSN messenger.
From research with in this capoeira group it is known that there are
approximately 100 students that train regularly across the different locations
during the week. So how can the 1001 nodes be explained?
The first point to make is that this figure of 100 students relates to the current
number of students that train within the group. This group has been running in
Manchester since 2002 and will shortly be celebrating its 10-year anniversary. It
is difficult to determine when this group was launched on Facebook, however,
Facebook itself came to the UK in October 2005 (Form 2012), It would,
therefore, be a conservative estimate to say that the group had been on Facebook
since 2007. Approximately 5 years, or half of the group’s existence in
Manchester.
In this time many people would have come and gone, so whilst there are
currently 100 students training the number of students that trained in the five
year period could be much larger.
The second point to make is that not all of these nodes represent the students of
the group. Other visiting teachers and friends of the instructor make up a lot of
these. As talked about earlier capoeira students do travel to other events around
the world and in the country. A large proportion of the thousand and one nodes

within this consists of students from other groups that have come to visit and
subsequently joined this group on Facebook.
A number of the nodes will be made up of people who are perhaps looking to join
the group or curious as to their activities and want to get to know more. A lot of
these nodes would have been removed as leaves and not represented within the
network graph shown in figure 11.
When referring back to figure 11 it can be seen that graph is divided into four
distinct areas as depicted by the different colours. The red areas generally depict
nodes from unrelated capoeira groups that are based outside. The orange area in
the top of the middle represents people from other capoeira groups based within
the UK. The large purple section consists of people from related capoeira groups
but based outside the UK. The remaining green section shows the UK based
students, the vast majority of which are from this Manchester capoeira group
over the years.
Method
In order to construct the capoeira networks and perform the case study a
combination of NodeXL, Netvizz and Gephi was used. NodeXL was used to extract
data from Twitter, YouTube and Flickr, whilst Netvizz was used to extract data
from Facebook. The data from both of these programs was then visualised within
Gephi.
One key feature of Gephi is the ability to append data. This means that data from
multiple sources can be combined into one file and the same nodes and edges
and not counted more than once preventing overlap.
With time larger networks can be constructed from smaller groups. This is how
the capoeira network was constructed during this research. After requesting to
join over 300 groups data extracted from 245. Four groups would not converge.

The breakdown of the groups can be shown in Appendix 1 below. It can be seen
from Table 1 below that there is a total of 121,753 nodes across the 241 groups
that converged.
Results and Analysis
Flickr Results
Figure12 Network Graph Showingthe Keywords Associated with the Term "capoeira" on Flickr
Flickr is a SNS based around the display of photographs. Using NodeXL to
perform a keyword search of “capoeira” on Flickr produces a graph that relates
the frequency with other keywords in photos uploaded.

This shows the keyword association with capoeira both directly related to
martial art itself and art forms relation to photography. It can be seen that the
capoeira node is the largest and that has strong ties with Brazil. The word Brazil
features twice spelt in both the English and Portuguese variations highlights this
fact. Other keywords not related to capoeira but to the medium of presentation
are also prominent. This includes the words Nikon and Canon that relate to
photography in particular.
This kind of result is useful for determining marketing strategy or trying to
address how to get the best impacts online with keywords. If societies, charities
and companies were looking for keywords to make their presence more
pronounced online, this kind of extraction could be a good starting point

YouTube Results
Figure13 Network Graph created from the "capoeira" tag within YouTube
The results from this graph shown in 13 compared to the graph produced in
Flickr is more difficult to navigate. Each node here represents a video in which
people have commented on or linked to and the label is a string of letters and
numbers not readily understandable by humans e.g. “MFgXmiL78Pg”.
When metrics from within Gephi are applied, however, the network can be more
useable. For example this graphics represents 878 videos and YouTube name of
the most frequent posters can be found as shown in figure 14 below. These can
then be used as the target for further research on YouTube.

Figure14 The Most Frequent Posters of Capoeira Videos onYouTube
Twitter Results
Figure15 Twitter network produced from the hashtag “capoeira” (from 100 tweets)

NodeXL was used to mine data from the last 100 and 1000 Twitter users that
used the word capoeira in a tweet. The networks that result from this are shown
in the figures above and below
Figure16 Twitter network produced from the hashtag “capoeira” (from 1000 tweets)
Edges are created in this case when a tweet is retweeted or replied to. It can be
seen that there’s a large difference between the 100 tweet and the 1000 tweet
network graphs. This is due to enough time passing the people to interact with
tweets and highlight relationships.
Of the 11 joined components in the 100-tweet graph two have a group size of
more than three. This is not enough to gauge how big an influence these nodes
have in the capoeira network on Facebook.

The 100-tweet graph shows a lot more detail that is useful for social network
analysers. There are three sizeable components that are independent from each
other. The green nodes indicate Twitter users that have more connections whilst
the pink nodes attached to them show how far the green node’s influence
spreads with each tweet.
Facebook Results
Figure17 Network Graph showing75453 nodes constructed from 241 Capoeira Groups
The network graph shown in figure above is the culmination of data mining from
Facebook via Netvizz. This is an amplified version of network graphs shown in

figure 11, which was based on one capoeira group. Graphs of this size are very
difficult to analyse visually and this is where programmes such as Gephi come
into their own. From this diagram many different groups as represented by the
different colour areas can be seen but even then it can be difficult to distinguish
one group from another and indeed how many groups that are. Each Facebook
capoeira user is represented by a circle, the largest belonging to the nodes that
are most connected.
If the labels are included in this diagram it quickly becomes difficult to read. This
is shown in the appendices in figure 24.
When running a variety of statistical metrics on Gephi the data we are looking
for can be found. Figure 18 below shows the top 10 most connected nodes within
the network above.
Figure18 Image to show the Top 10 mostConnected Nodes from the Constructed Capoeira Network
Gephi is also able to confirm the existence of the giant component that consists of
68448 nodes (91%) of the population. This confirms the introduction to network
theory as described. The average network path here is 6.9 which is in agreement
with the work performed on Microsoft Messenger by Lescovek and Horvitz.

Table 1 Key Values from Netvizz Data Extraction of Facebook Capoeira Groups
Simple Node Count 121753
Number of Unique
Nodes 75453
Number of Groups
Joined 245
Groups converged 241
Shrinkage 0.38
Raw Average Size 512
Unique Average Size 317
When looking at the list of the most influential users within the capoeira
network on Facebook evidence can be seen that selective algorithms guided
group selection. The node list of the large Facebook group shows that
C.M.Parente is most connected node within 75,000 other nodes. He is followed
by C.M.Papa-Leguas. This is the same as the Manchester Capoeira group show in
figure 11.
It should be noted that the Manchester capoeira group was one of the first
groups to be joined as part of this research. Lots of the 245 groups this research
became a member of, the Manchester group was 4th. It is also the first group to
have a substantial number of members with 1001 nodes compared to 150, 68
and 106 for the first, second and third groups joined. It is, therefore, conceivable
that’s Facebook’s Edgerank algorithm has “guided” the rest of the capoeira
groups that were displayed during the search.
In order to assess the impact of this separate data collection exercises could have
to be performed. This time care would have to be taken to ensure the groups
found did not contain the members C.M.Parente and C.M.Papa-Leguas. If their
names showed up under similar circumstances is this would indicate that they
are well connected within capoeira the Facebook community as a whole. If,

however, they did not show up or if their presence was much smaller it could be
suggested that the Edgerank had a profound effect on this research.
Shrinkage
Table 1 above shows some key data taking during the course of this research.
One of the most interesting points to notice is that 75453 represents the number
of total nodes in their final networking diagram but is less than the simple node
count (121753). This is a concept that is introduced during this research as no
information can be found about it elsewhere.
This is explained by the fact that people are free to join as many groups as they
wish on Facebook only limited by the approval process. This means that the
same person maybe a member of several groups on Facebook as such their
number will become to more than once in raw data. During this research only
one Facebook account were used so in this case one user is a member of 245
capoeira groups. The fact that software such as Gephi only counts each user once
exposes this “shrinkage”.
This is defined here as:
“the fraction by which the simple node count exceeds the actual number
of nodes”
In this case the simple node count is 121753 that feeds into a graph of 75453.
The shrinkage number is given here by 1 – (75453/121753) = 0.38.
A shrinkage number of 0 indicates that each node is a member of a different
group and that each group is discrete, whilst a number close to 1 suggests that
everyone is a member of everyone else’s group. Having a high or low shrinkage
number is not necessarily a good and bad thing but is an indicator of the
dynamics within the network. If one wanted to transmit a certain piece of
information from group to group it would certainly be easier with the networks
that consist of groups with higher shrinkage numbers.

Capoeira Vibe Case
The final part of the results analysis is a brief case study based on the capoeira
community Capoeira Vibe that exists on all the four SNSs. The intention here is to
give a real high level example of the information can gain from the data mining
process in the paper. Facebook currently does not allow fan pages to be made
mined. So it should be noted for balance that Capoeira Vibe has well over 11,000
likes and would represent its largest network out of these four.
Figure19 Network Graph Showingof the "Capoeira Vibe" Network Communityon Flickr
The Flickr group is smallest of the Capoeira Vibe networks followed by YouTube.
This may be because these sites focus on the production and posting of material.
Based on these findings it would a recommendation to expand their networks on
these sites prior to posting any more data. We have seen that the capoeira
network on Facebook has a shrinkage number of 0.38, which means the
information should pass quite freely within this network. In order to facilitate
the passing of information i.e. new photos and videos, more links should be
made.

The twitter group shown in figure 22 is made up of 1359 nodes, however, they
have 249 followers. This shows they are simply not engaging with the audience
as much possible. The advantage of the graph shown in figure 22 is that they can
now target key members of the network and can use things like instant
messaging and retweets to prioritise which users to form allegiances with.
Figure20 Capoeira Vibe level 1.0 YouTube Network

Figure21 Capoeira Vibe Level 2.0 YouTube Network

Figure22 Capoeira Vibe Twitter Network
Ethics
It can be seen above that large amounts of data can be created, collected,
analysed quickly. This has obvious merits when compare to more traditional
methods of data collection such a surveying and interviewing, especially when
there are time and economic constraints. When this data is collected by surveys,
however, people are consenting to its usage in this manner. One key concern
with data mining is that people allow the data to be used online in one context
but the data is extracted and used for another(Van Wel & Royakkers 2004).

When users signed up to a SNS’s they typically sign an end user license
agreement (EULA). The vast majority of people don’t read these and agree in
haste simply to start using a service. The first line of Facebook’s EULA is “Your
privacy is very important to us”(Herman & Ullyot 2012). It then moves to
explaining how the user owns their own content but they allow Facebook to use
it as they wish. This is where the information from data mining can be exploited.
Data mining happens as the people, perhaps unwittingly, allow it to happen
maybe due to ignorance or forgoing privacy simply to be able to use services.
Of course the ability to do something does not suggest that it is ethically right. By
setting up the EULA’s the social networks effectively absolve themselves from
the ethical issues of data protection.
The data collected during the course of this research was only restricted by time
and, in the case of Twitter, the concern that accessing data might adversely affect
their bandwidth.
It is clear that those wanting to use data mining are the ones that are carrying
the moral flag. Any information collected during the course of this research has
been thought about such that private data cannot be exploited.
The data collected here has been for the purposes of establishing patterns
structure. It is arguably the next steps following the data mining the pose the
largest threat. If data mining was used to establish whom to contact for further
research then this becomes little more than a telephone directory. As the
discussion moves into the qualitative phases does the concern with ethics start
to lessen. Here communication is on a much more personal level and individual
permission simply has to be granted. This can otherwise be seen as spam
contact, which can be deemed unethical.
Data mining from SNS is still in its infancy and will no doubt continue to grow
given its merits. Its usage needs to be monitored now to ensure that it is not
exploited in a negative way in the future. It is clear that at this stage moral
implications are very much in the hands of those extracting the data.

Quantitative vs. Qualitative
The data mining processes and the network graphs and analysis produced are all
quantitative. A lot of data can be collected in a short period of time and this can
be used to establish trends help steer the direction that organisations may
follow. Using the keyword analysis from Flickr, for example, can be used to set up
a websites metatag infrastructure that is important for search engine
optimisation (Jones 2011).
The problem with pure quantitative analysis is that patterns and trends can be
seen and predicted. It can, however, be very difficult to understand what and
why things are happening. In the analysis of the Manchester capoeira group
(Figure 11) the breakdown of the different regions of the graph was only
possible after conducting a short interview with C.M.Parente. Without this it
would be very difficult to understand why the communities have collected in the
manner they did.
Indeed the main outcome of the Capoeira Vibe case study is that they now, in the
case of Twitter, YouTube and Flickr, have a much clearer picture of the networks
around them. They can also of course draw information from the “general”
network analysis created from Facebook, twitter and Flickr. In the research
performed by Trompeter on social media tactics (Trompeter 2010) a key thread
emerged about reaching and connecting with people. This data mining helps to
find those people and the next stage would be to connect with them.
In the book “Groundswell” the authors research produced the “Social
Technographics Ladder”, which is reproduced below.

Figure23 Social Technographics Ladder based onU.S. Adults (Li & Bernoff 2011, p.43)
Each step higher on the ladder indicates a greater level of involvement in social
media. The use of data mining for SNA helps to identify these people and where
they are. Qualitative measures will connect to these people and further
understanding, which may yield results.
This research will not go further into the discourse of qualitative and
quantitative. The output from this paper has been quantitative but that should
not be mutually excluded from qualitative. There has been a line of approach that
suggests a mixed method analysis would be most suitable and fitting to SNA.
“Network structure is not the whole story… we need to supplement
methods of formal network analysis with qualitative observations about
what is “going on” within a network.” (Crossley 2010, p.18).
“Qualitative approaches add an awareness of context which aids the
interpretation of network maps and measures; they add an appreciation
of the perception of the network from the inside; and an appreciation of the

content of ties in terms of quality, meaning, and changes over time.”
(Edwards 2010, p.24)
In the real cases it is suggested that both methods are used and to the ability of
the time, resources and knowledge base within each organisation.
Conclusion
It can be seen during the course is recess that there are several ways to collect
data from SNS’s. The type of data collected and sites chosen to mine from should
be well considered, as each will return with different types of usable information.
For those inexperienced with computer programming the use of software such
as NodeXL and Netvizz for data mining purposes are good options.
There are often time and budgetary restraints when conducting market research
but given the short learning curve with these programs even those uninitiated
can yield meaningful results. This ties in with one of the key benefits of
marketing by social media i.e. that it is cost-effective (Trompeter 2010).
This is particularly true given as it can be used for research purposes prelaunch
to help establish marketing strategy on sites and such as YouTube and Flickr to
for finding keywords to use online. Once a company or product has been
established these data mining metrics and networking techniques can be used to
monitor progress online and plan future steps.
When it comes to this research, aims and objectives were presented at the outset
of this paper. Each has been addressed to varying levels constrained by the
binding parameters of this research and the conflicting intention of producing a
standalone paper. Many of the aims could form the basis their own research. It is
felt in this case however that an overview would be more beneficial at this stage.

Appendix 1: Table to show the Number, Name, Group ID and Number of Nodes as Extracted by Netvizz
CAPOEIRA
GROUP
NUMBER
FACEBOOK CAPOEIRA GROUP NAME FACEBOOK GROUP
ID
NUMBER OF
MEMBERS
NODES
FROM
UNMINEABLE
GROUPS
245 CAPOEIRA MUZENZA KOREA 188938991204060 298
244 Cordao de Ouro Moscow 217904894908696 28
243 CAPOEIRA MANDINGA MEXICO 73554666612 403
242 Capoeira Meeting Copenhagen 61563872205 302
241 ABADA Capoeira UK 117537624347 373
240 Malandragem Capoeira 14463235598199 1468
239 Ryerson Capoeira Club 257873730977742 510
238 Real Capoeira 87587742228 250
237 Núcleo Abaeté Rede Anca Capoeira 385200501490881 284
236 Grupo Capoeira Brasil - Professora Mariana "Potiguara" 217949151486 379
235 Grupo Axé Capoeira 2248929446 1377
234 Capoeira sul da bahia CM MAXUEL, Monitor dexter 382019125175541 542
233 GRUPOS DE CAPOEIRA EN MEXICO 136198343113436 622
232 Grupo Caymã Capoeira 282348378525310 1494
231 Capoeira SDB Contra Mestre Maxuel, Profesor Estagiário Eddu.. 58338428093 325
230 I love capoeira cdo 182311485177255 487
229 CAPOEIRA Y ACONDICIONAMIENTO FISICO BOGOTA 106388989487731 296
228 GRUPO BERIBAZU E AMIGOS DA CAPOEIRAGEM 187972871213976 826
227 Capoeira Sul da Bahía - C Mestre Maxuel, Prof Estagaria Alena 36077561796 302

CHILE
226 Capoeira Angola in Manila 124826510863308 231
225 Capoeira "Body & Soul" di Mario Collina 253970458014301 554
224 Porao Capoeira Vienna/Austria 130157855172 477
223 Capoeira Karkara 110588072355664 290
222 Capoeira Q Roda 223415177693922 194
221 Axé Capoeira Bursa 148746176508 451
220 CAPOEIRA - Oficina da Capoeira Internacional "Venezuela" 114404815258982 259
219 Capoeira Senzala (professor Palhaco-Belgrade) 41992525089 369
218 Capoeira Friends 102512993162401 325
217 Capoeira Nagô Malta 23503931496 934
216 Capoeira Videos 217041721655963 527
215 Escola Nestor Capoeira 175658252458533 270
214 Casa Da Capoeira Knysna 21790256882 666
213 Comunidade Capoeira 148020055323480 278
212 Capoeira Brooklyn - Raizes do Brasil 12292780174 203
211 Capoeira Malungos Bayonne 39969985608 592
210 Aú Capoeira New Zealand 19690984056 288
209 Cantigas e Documentarios de Capoeira 279741352133380 1104
208 Capoeira (Finland) 6205335078 203
207 Capoeira Polska 473542129336661 515
206 CAPOEIRA AGUA DE BEBER VENEZUELA 41172345168 519
205 Escola Brasileira de Capoeira, Philippines 27691384580 677
204 XANGO Capoeira Australia 7436426726 614
203 SENZALA MACEDONIA-MESTRE PULMAO ( Capoeira Skopje ) 44283313844
Data extraction not
converged 4959

202 Capoeira Kościerzyna 175941112541870 270
201 Capoeira Senzala Genève 23039013657 204
200 Ginga Firme Capoeira Makassar 80141919030 384
199 CAPOEIRA MEU DEUS RANCAGUA 253128554776427 307
198 Capoeira Vida 188271654538689 935
197 Capoeira Training Brighton 131365310324937 287
196 Abadá Capoeira Milano 28377257445 438
195 Capoeira Filhos de Angola ~ Lefkada 175698712558150 255
194 Capoeira Cordao de Ouro - South Africa 11284290815 227
193 Filhos De Bimba Escola De Capoeira Lebanon 61878158913 588
192 Capoeira raça 156914091014346 239
191 Capoeira Senzala Scotland 4959664847 356
190 Capoeira Senzala Lara (Venezuela) 56762910082 232
189 Abadá Capoeira Bogotá 174064432704 448
188 FILHOS DE BIMBA ESCOLA DE CAPOEIRA STUTTGART 194794585908 597
187 Capoeira Sobreviventes 2345044102 407
186 Capoeira Cordão De Ouro Costa Rica 136361619738448 337
185 Capoeira Nago Brasil 168077533231588 308
184 Capoeira TRIARTE Genova 113640695314427 227
183 Capoeira Angola Center Finland 11110057026 427
182 Association Swedendê & Capoeira Senzala - Professor Coqueirinho 54700188474 569
181 Capoeira Amazonas Split 16832446806 212
180 Axé Capoeira—Czech Republic 132752446753272 247
179 Grupo Capoeira Males - Burlington 125210507601369
Data extraction not
converged 1095
178 Grupo de Capoeira da Angola Istanbul 128728250490174 360

177 Rodas e Eventos de Capoeira em SP 154380194685843 862
176 Gruppo Capoeira San Marino 307586172594387 338
175 i want to learn capoeira (Malaysia) 165425803469895 268
174 Grupo de Capoeira Angola Menino Quem Foi Seu Mestre - London 340454615983173 343
173 Soluna Capoeira 28969814448 738
172 GRUPO CAPOEIRA CHALKIDA 39546508542 400
171 Mundo do Capoeira 343881525679887 1004
170 Grupo Capoeira Brasil- New York: Formanda Colibri 311052822323721 809
169 Abadá-Capoeira ::: Designs 361378350570801 737
168 Capoeiraskolen Senzala 46510288650 283
167 Capoeira Ringsted Grupo Malungos 56416035736 329
166 CAPOEIRA RAÇA E FUNDAMENTO "SERRA NEGRA" PRFº JAÚ 239679632776568 261
165 Escola de CAPOEIRA GINGA CARIOCA 28634226177 175
164 CAPOEIRA om formiddagen på St. Kongensgade 79 329466953808780 190
163 CAPOEIRA 150509121659291 467
162 CAPOEIRA BELGRADE-CMPULMAO 10219317260 1009
161 Capoeira Brasil - Bogotá 13283446207 510
160 Capoeira Puerto Rico Senzala 111639975573687 219
159 Capoeira Aché Brasil Malaysia 135483606485823 292
158 Capoeira Kuwait-‫را‬ ‫وي‬ ‫اب‬ ‫ك‬ ‫ت‬ ‫وي‬ ‫ك‬ 48035048877 279
157 Capoeira Belfast 181860001000 209
156 Capoeira Brasil Hermosillo 174108045980534 230
155 Capoeira Cyprus 297572139539 247
154 Group Capoeira Brasil - New Zealand - 278509383840 654
153 Capoeira Senzala de Santos 225993607412207 469
152 CAPOEIRA 177360938985380 307

151 Capoeira Ijexá 173148365299 213
150 Capoeira Gerais Madrid 143121215744995 1920
149 CAPOEIRA - UFRJ 171081326282080 1144
148 Capoeira Mersin 114280165362620 307
147 Capoeira Malungos Saint Etienne - France 141196022583911 709
146 Capoeira Senzala Montreal 250074501433 216
145 Capoeira Brinquedo de Angola 155219664543731 257
144 Capoeira Raizes do Brasil Milano 33398315109 643
143 CAPOEIRA MANDOU CHAMAR ULM GERMANY 449570235082920 312
142 Capoeira Calédonia, Energia da Bahia - Nouméa - 35682791319 453
141 CapoeirArab 156542164439065 393
140 Capoeira Senzala do Caribe 94715705127 894
139 ASOCIACION ARGENTINA DE CAPOEIRA 8716951310 1713
138 Capoeira in Michigan 2589775177 935
137 Capoeira Angola Israel - ‫קפואירא‬ ‫אנגולה‬ ‫ישראל‬ 297434566996612 709
136
Professor Cebolinha CORDÃO DE OURO CAPOEIRA - Newark-NJ-
USA 406607306047829 1203
135 Capoeira Cordão de Ouro - Bonneuil 522128287802986 283
134 Capoeira Conviver 4125218546 310
133 Capoeira Volta ao Mundo brazil sweden 2407568628 283
132 Capoeira Brasil Cayman Islands - Instructor Koé 236552959187 374
131 Capoeira Malungos Paris - Association Senzaleiros 7538366468 436
130 Capoeira BALI 129610587101010 239
129 CAPOEIRA 269552989741226 266
128 ECAM (Escuela de Capoeira y Artes Mixtas) 315767145181306 1173
127 Capoeira in Toronto 2213115016 474

126 CAPOEIRA NATIVOS TUNJA - GARAGOA 263013087148341 274
125 Capoeira Malungos Ambérieu-en-Bugey 405396279522761 129
124 Capoeira ALEGRIA 179497432074997 255
123 Capoeira de Camaçari 375681459155880 443
122 CAPOEIRA GINGA DE MAPUTO 92676097159 654
121 Capoeira 198617790151751 399
120 Capoeira Força Natural 27637657908 246
119 Capoeira Passo a Frente 150336321678724 250
118 Grupo MATUMBÉ Capoeira - BARCELONA-SPAIN 368368470689 673
117 Capoeira Sul da Bahia Vienna Professor David 100311471224 279
116 Mundo Capoeira Türkiye - Mundo Capoeira Turquia 5745007875 1093
115 Capoeira "N" Surf Morocco 124449746100 277
114 Capoeira Estilo Livre 367768209937829 561
113 Capoeira 321810367891948 955
112 CAPOEIRA MUSIC ! 175857075765440 860
111 CAPOEIRA MAROC 13963475108 949
110 Capoeira Mandinga Taiwan 158763090818287 286
109 Capoeiranagô Berlin alemanha Professor Rogérinho 339334099467054 577
108 CAPOEIRA ARTES DAS GERAIS BRASIL MESTRE MUSEU FICAG 116408905097958 304
107 Capoeira Del Bruto Genève 251727598259443 368
106 Capoeira Ache Brasil Whistler 305421630796 248
105 Capoeira India 24905600976 476
104 Capoeira Cordão de Ouro Indonesia 15750918795 1095
103 CAPOEIRA - FORÇA JOVEM (CAMPO GRANDE - MS) 385510051471770 368
102 Capoeira 310291139019831 344
101 Capoeira Angola 132756643447610 247

100 Músicas da Abadá-Capoeira 246876338619 1954
99 Capoeira 182151925232852 437
98 Capoeira 173728919335311 1479
97 Capoeira Natural Do Brasil 57416365662 826
96 Capoeira Angola Center of Mestre João Grande - Oakland 144322595611492 353
95 CAPOEIRA DUBAI 202860073566 609
94 Capoeira Street Rodas Club 88497857586
Data extraction not
converged 344
93 Capoeira Lapinha 416658591683370 490
92 Capoeira Filhos Da Bahia Australia 2362794567 1565
91 CAPOEIRA SENZALA NOVI SAD, SERBIA 63798471319 790
90 CAPOEIRA CIDADÃ 131298673593799 320
89 CAPOEIRA MOVIMENTO 224446160984111 301
88 Anauê Capoeira Internacional 136632315120 985
87 Cordao de Ouro Argentina 165325643526339 52
86 CAPOEIRA SUL DA BAHIA KØBENHAVN DANMARK 121672330541 309
85 Capoeira Batuque 49838989530 912
84 Capoeira Nova Era Mexico 113239375415803 376
83 Capoeira Aguascalientes 120202567998906 298
82 Capoeira Bem-Vindo 168608506486901 338
81 Axe Capoeira Türkiye 5464803047 1686
80 capoeira TwisT ponorogo 146387224975
Data extraction not
converged 375
79 Capoeira Ballymena 382801128443913 68
78 Capoeira en Querétaro 345555565532083 105
77 Capoeira in armenia 129554777132684 1125

76 Capoeira Senzala Lyon 42847059088 297
75 CORDAO DE OURO COLOMBIA 45725838389 685
74 Capoeira Sul da Bahia - Washington D.C. 289485185402 363
73 CAPOEIRA NOVA ARTE 467158856636578 288
72 Capoeira Rijeka, Jacobina Arte Croatia 47105079044 355
71 Capoeira & break dance 231690590188039 233
70 Capoeira Angola FICA - Bogotá 10742924786 661
69 Capoeira Brasileira Montreal 2627110574 449
68 Capoeira Acrebrasil Milagro 156360181121850 408
67 Capoeira Senzala - Contramestre Steen - Professor Axé Canarinho 7481173828 643
66 Capoeira LDMUNAM (Cabeleira) 146698528752388 386
65 Capoeira Guerreiro Orixas 103209925238 363
64 Capoeira Angola Nottingham 60638711667 339
63 Capoeira 345054108914651 56
62 Capoeira Angola Center Italia 94821955561 368
61 Capoeira Angola Center Sérvia - Contra-Mestre Marquinho 132801996810260 432
60 Capoeira Ioannina - Companhia Pernas Pro Ar 53432940124 716
59 Capoeira De Nazareth Cordao De Ouro Israel 15886395195 699
58 Capoeira Nativos de Minas 4386281365 417
57 Capoeira Raça İtabuna - Bahia - Brasil 386805337998363 776
56 Capoeira Bulgaria 53282511878 720
55 Capoeira Infantil 200096850038709 1646
54 Capoeira Moldova 433028646709900 560
53 Capoeira AMAZONAS - Hrvatska 6074204063 584
52 capoeira surfista - ‫קפוארה‬ ‫סורפיסטה‬ 38643985389 326
51 Capoeira-Music.net 225102747556546 570

50
Capoeira Origens do Brasil UK - Southampton- Bournemouth -
Portsmouth 19775543856 528
49 Capoeira Jerusalem ‫קפוארה‬ ‫ירושלים‬ 27033528099 934
48 Capoeira Angola Center of Mestre Joao Grande - New York 33918702846 1119
47 Capoeira Mineira 170275553055302 619
46 cordao de ouro sapporo do japao 162369700451798 12
45 Capoeira Sevgisi 229788467082160 323
44 Capoeira Topazio Rieti 51748450901 861
43 Capoeira Associação Sérvia - Professor Touro Branco e Alunos 189648850746 923
42 Capoeira Bristol- Claudio Campos 2333889907 642
41 Cordao de Ouro Norwich Capoeira 7421940457 268
40 Volta por Cima, Cordao de Ouro, Turku 113084175538 73
39 Capoeira Plantando Dendê 414665775257111 241
38 Capoeirando 313048142084018 437
37 roda 153865851333143 58
36
μπαράκι Μαρίας στις Γούβες (ή αλλιώς πειρατικό ή αλλιώς
λεωφορείο ή αλλιώς) 112277615476421 756
35 Contra Mestre Turbina - Norway 232402870103870 407
34 Capoeira Picture 437611929590499 704
33 Capoeira World 353301284719697 1394
32
Capoeira Iowa - CORDÃO DE OURO C. M. Cabeção and Intrutora
Tiririca 300009700073805 771
31 Capoeira Birmingham Cordao de Ouro 365929986778646 81
30 Capoeira Malungos Landes et Béarn 278612768888080 1089
29 CAPOEIRA JERICOACOARA 226519449716 203
28 Come and Play 138134466263978 139
27 Negoteta Capoeira -Guardioes Brasileiros 174957519203487 134

26 Cordao De Ouro Scotland 199800916760341 146
25 Capoeira Cordao de Ouro Milano -BAMBU- (www.capoeiracdo.it) 190470224308554 483
24 Capoeira Cordao De Ouro Manchester Children's classes 138546192907722 54
23 Capoeira i Bergen :) 242864262413466 52
22 Cordao de Ouro Wirral 216025318428937 44
21 capoeira cdo Marseille 202338545265 252
20 Capoeira Malungos Edinburgh - Scotland 100830409973949 252
19 Capoeira Cordão de Ouro Cheshire 140611855957168 115
18 Capoeira Ceara 2230017764 580
17 Afro Ritmo CDO - Capoeira 120447254654796 196
16 Cordão De Ouro Livorno 118805631471068 530
15 Cordao de Ouro Athens 42033326349 1076
14 Capoeira in Lancaster ( UK ) 57711742048 45
13 Cordão de Ouro Oslo- Instrutor Pirucão 442088410724 205
12
‫לוח‬ ‫ההודעות‬ ‫של‬ ‫המרכז‬ ‫הישראלי‬ ‫לקפוארה‬ - Cordão de ouro Israel
Mestre Edan 133660927258 736
11 CAPOEIRA IN CYPRUS "Cordao de ouro Cyprus" 255023815847 1566
10 Cordão de Ouro Barcelona 54752581524 161
9 Italia Centro Di Capoeira 163960295258 365
8 Cordao de Ouro Capoeira Sheffield 2259263842 219
7 Capoeira Nottingham CDO 47201706570 240
6 Cordão de Ouro Capoeira Crete 34564813057 1447
5 Capoeira Batizados & Workshops in Europe 55370608206 894
4
Capoeira Cordão de Ouro North West UK (Manchester and
Liverpool) 2333577653 1001
3 Capoeira CBF 38593533333 106

2 University of York Capoeira 32824463126 68
1 Cordao de Ouro Capoeira Derby 2221308745 150
Total Number of
Nodes 121753
Number of Unique
Nodes 75453
Number of Groups
Joined 245
Groups converged 241
Shrinkage 0.38

Appendix 2: Python GitHub Repositories for Social Media Sites
The official online compendium for Mining the Social Web (O'Reilly, 2011)
https://github.com/ptwobrussell/Mining-the-Social-Web
A Python library for accessing the Twitter API
https://github.com/tweepy/tweepy
Facebook.py is a Python Client Library for the Facebook APIs. The goal is to
support all Facebook APIs located at http://developers.facebook.com using only
standard python libraries.
https://github.com/semyazza/Facebook.py
Facebook Platform Python SDK
https://github.com/pythonforfacebook/facebook-sdk
Python client lib for Facebook's new Graph API
https://github.com/iplatform/pyFaceGraph/
Facepy makes it really easy to interact with Facebook's Graph API
https://github.com/jgorset/facepy
A micro api client for writing scripts against the Facebook Graph API
https://github.com/facebook/fbconsole
Appendix 3: Other Web Resources
NodeXL Download for Windows
http://nodexl.codeplex.com/
Netvizz Facebook App

http://apps.facebook.com/netvizz/?fb_source=search&ref=ts
Gephi Download and Documentation
http://gephi.org/
Appendix 3 Other Network Images
Figure24 Network Graph of 241 Capoeira Facebook Groups with Labels

Figure25 Capoeira Vibe Level 1.5 Network on YouTube

Figure26 Image showingthe Giant Component from the Facebook capoeiranetwork. In spite of the
different colours depicting different groups each node canbe connected to each other. The longest
distance between any two nodes is 22. This contains 68448 nodes compared to 75453 inthe
complete network
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