The document summarizes a student's analysis of the Twitter profiles of various K-pop artists and media accounts related to K-pop. The student created a Twitter profile to follow accounts related to K-pop such as specific bands and a popular K-pop media site. Network graphs were generated from the data to show relationships between the followed accounts and their followers. The graphs revealed the media site had the largest following of over 2 million and loose connections between other accounts. The analysis provided insight into the popularity and spread of K-pop content online.

1. Digital Media and SocialNetworks
Coursework 1: User profile crawling
Marzena Chmielewska
Twitter Profile Creation
The first step to obtaining the data required for analysis was to create a Twitter profile. Because I already
have an existing Twitter profile which was created about five years ago, I have decided to use that
instead. In addition I already follow some accounts that are relevant to the topic that I will be selecting.
Data Analysis Topic
For my data analysis I have chosen the topic of Korean pop music, commonly abbreviated and also
referred to from this point in the report as K-pop. This is a genre of music that has become increasingly
popular in the West in the last few years with artists such as Girl’s Generation or 2NE1, and notably hit
mainstream awareness in 2012 with the song Gangham Style by Psy. This rise in Korean music and
culture has been referred to as the Korean Wave, or Hallyu in Korea (Yong Jin, D). Thus I believe this
would be a interesting topic to conduct an analysis, due to it’s increasing popularity and influence within a
subset of Western culture. The graph below (Figure. 1) depicts the interest of the term K-pop in the last
few years, and it’s peak between 2012 - 2013.
Figure 1. Google Trends, Interest in ‘K-pop’.
A following look at the interest of the one of the most popular K-pop girl groups also reflects a similar
correlation in peak and subsequent stabilization of interest (Figure. 2).

2. Figure 2. Google Trends, Interest in ‘Girl’s Generation’.
Profiles followed for the needs of the analysis:
Username Following Followers
Ailee (@itzailee) 212 768,000
에릭남 (Eric Nam) 382 175,000
SNSD Korean (Girls Generation)
(@snsdkorean)
22 155,000
GOT7(@GOT7Official) 46 663,000
Kim Hyun Ah (@4M_hyunah) 47 471,000
PSY(@psy_oppa) 616 3,920,000
K.will(@Thsm1) 154 299,000
allkpop(@allkpop) 524 2,100,000
G-Dragon(@IBGDRGN) 109 4,130,000
Taeyang(@realtaeyang) 96 1,800,000
To have variety of profiles, I chose both more popular and less popular artists. As it can be seen, the
bigger the artist got in the West (PSY, GDragon are both fairly known abroad) have more followers than
artists who are mainly known in their homeland (look at: K.Will or Eric Nam). Recently aside of music,
some artists are recognised due to their influence in fashion and therefore make the K-pop industry more
known to bigger circles of people - even if a lot of fashion lovers do not listen to his music, a lot of them
know GDragon for his famous fashion looks for the most prestigious fashion magazines in the West
(Maeland, A. 2014).

3. Data Retrieval
After establishing the Twitter account that I would use as the basis for my analysis I proceeded to
implement code to retrieve the relevant data that I believed would be sufficient for analysis. This
consisted of retrieving different elements such as information about followers and the profiles that I am
following. The data was retrieved using various packages in R. For the network graphs the package used
was networkD3 (d3network in previous versions of R) based on JavaScript network graph creation code.
Script 1:
Script 2:

4. Script 3:
Graph Analysis
Through the following graphs I can establish different trends about my social influence and the correlate
this to the topic.

5. The figure above shows my profile and users who follow me back. Because the profile was created a long
time ago and used for a while, the amount of followers is way higher than was needed for the analysis.
The graph is directed and centralised due to the type of data portrayed above.
Graph shown above though, shows the out degree of the network. All of the account connected to my
user profile here, are profiles that I follow as well as their usernames and their followers count. As we can
see it’s still one node degree with all of the profiles due to the nature of the dataset analysed.

6. Because the network is varied as are the types of profiles I follow, the graph created for the network of
following and their followers is indeed big and complicated. Due to my various interests and connections,
the actual network is not centralised and strongly connected.
Social Behaviour and Influence
Some conclusions can also be derived about my social behaviour. From looking at the graphs I can
determine that many of the profiles I follow are connected to a lot of user profiles. Especially when it
comes to the profiles of artists, the amount of users observing them (even if their profiles are mainly in
their native language which is Korean) are gaining in popularity among people from all around the world.
The most popular western K-pop news and celebrity gossip site named allkpop has 2.1M followers; aside
of having readers on their actual website and Facebook fan page.
The biggest network on the left is mentioned above @allkpop profile, which is connected to 2 profiles
which i also follow. Aside of that there is high amount of loose connections and high degrees between
other edges. The graph was generated to show how big, compared to other users that my profile follows,
is the profile which posts about celebrity only happening between korean celebrities.
References
Yong Jin, D. 2012.Hallyu 2.0: The New Korean Wave in the Creative Industry. University of Michigan International
Institute Journal Volume 2 Fall 2012.
Google Trends.2015.Google Trends.[Online]Available at: https://www.google.co.uk/trends/.[Accessed 03 April
2015]

7. @Swirlingetiude.2015.Marzena (@Swirlingetiude) on Twitter [Online] Available at: https://twitter.com/Swirlingetiude.
[Accessed 03 April 2015]
R Studio - httr network visualisation [Online]Available at: http://blog.rstudio.org/2014/12/18/htmlwidgets-
javascript-data-visualization-for-r/[Accessed 02 April 2015]
Maeland, A. 2014. The HYPEBEAST Magazine Issue 6 - G-Dragon’s Global Coup [Online]Available at:
http://hypebeast.com/2014/3/the-hypebeast-magazine-issue-6-g-dragons-global-coup.[Accessed 03 April 2015]