NMIX 4200 Final Paper Report

TomorrowWorld
An analysis of TomorrowWorld and @belugaPod through social media.

By: Patrick Grant, John Paul Stott, Lewys Evans

I. Executive Summary
This paper discusses the main issues and users related to the TomorrowWorld music
festival. It also analyzes our Twitter account, @belugaPOD and recommends how we can
improve our social media presence. We used NodeXL to collect data from Twitter, YouTube, and
Wikipedia. We also analyzed our online presence and website traffic using Google Analytics.
Through Twitter, YouTube, and Wikipedia, we discovered how global TomorrowWorld is.
We also discovered that the majority of the conversation pertained to specific songs and
performances from the festival. Despite being collected a month apart, the two Twitter data sets
we collected varied in the most prevalent topics discussed. While conversation in the first data
set mainly focused on issues from this year’s festival such as performances and events that took
place, the conversation in the second data set showed a shift in focus from this year’s event to
next year. Analysis of our own Twitter account showed that we were most effective in engaging
with an audience when we interacted with artists under our King Beluga music label. Through
Google Analytics, we learned that the most important visitors to our site in terms of bounce rate,
pages/visit, and time on site came from Sound Cloud.
In order to improve our presence in the TomorrowWorld network as well as our own
social media presence, we came up with a number of suggestions. In order to increase our
presence in the TomorrowWorld network, first we need to interact with more important users and
more often. Hopefully through this repeated interaction, the users will recognize our account and
help us increase our reach. Our second suggestion is that become more involved in the smaller
conversations that make up the Twitter and YouTube networks. The only way we will improve
our presence throughout the entire network is by first establishing our presence in the sub-

networks. As for increasing our social media and online presence, we need to tweet more
creative content such as videos and songs and we must increase our presence on Sound Cloud.
II. Goals
We had a number of goals and questions coming into this project.
1. Who are the most important users tweeting about TomorrowWorld? How have they
changed throughout our data sets?
2. Who are the users tweeting in this network? How have they changed throughout our data
sets?
3. What topics are people tweeting about? How have they changed throughout our data
sets?
4. What is our role in this conversation?
5. What are the most important and popular YouTube videos regarding TomorrowWorld?
6. What are the most important and popular videos about?
7. What topics are related to TomorrowWorld on Wikipedia?
8. What affect has our social media presence had directing traffic to our website?
9. How can we improve our social media presence?
III. Social Media and Google Analytics
i. Twitter Topic Network
Our goals/research questions for our Twitter topic network research include:
1. Who are the most important users tweeting about TomorrowWorld? How have they
changed from the first data set to the second?
2. Who are the users tweeting in this network? How have they changed from the first data
set to the second?
3. What topics are people tweeting about? How have they changed from the first data set to
the second?

4. What are the most popular topics people are tweeting about? How have they changed
from the first data set to the second?
Methods
Data
We collected two different Twitter data sets from two periods of time. Nodes in the
Twitter network are users while edges are tweets, mentions, or replies from one account to
another.
For the first data set, 8,133 Twitter users and 14,460 tweets that contained
“tomorrowworld” from between Saturday, October 11, 2014 to Monday, October 20 2014 were
captured. Also collected was user statistics (e.g. profile description, # of followers), and tweet
statistics (e.g. tweet date, URLs in tweet). We collected data using NodeXL’s Twitter Search
importer, which identifies all Twitter users and tweets that included “tomorrowworld.” This
created a topic-network about TomorrowWorld.
For the second data set, 7,939 Twitter users and 13,298 tweets that contained
“tomorrowworld” from between Wednesday, November 5, 2014 to Friday, November 14, 2014
were captured. Also collected was user statistics (e.g. profile description, # of followers), and
tweet statistics (e.g. tweet date, URLs in tweet). We collected data using NodeXL’s Twitter
Search importer (Hansen, Shneiderman, & Smith, 2011), which identifies all Twitter users and
tweets that included “tomorrowworld.” This created a topic-network about TomorrowWorld.
Measurements
Identifying important users

We identified important users in our topic network in two ways. The first set of
important users was determined based off their in-degree (the number of links directed to a
person). Chosen were individuals from clusters 1 through 8 from the first data set and clusters 1
through 9 from the second data set with high in-degrees relative to their cluster. The second set
of important users was determined based on betweenness centrality (how important the user is in
connecting clusters within the network). Chosen were 5 users with the highest betweenness
centrality throughout the entire network.
Content Analysis
We classified Twitter users into categories based on their location and what role they
played in TomorrowWorld. The 3 options for location were: (2) Inside the U.S.; (1) Outside the
U.S.; (0) Unknown location. In order to determine the user’s location, we examined the location
associated with the user’s profile. The language used on the user’s account was also taken into
consideration when determining location.
The 5 options for the user’s role in TomorrowWorld were: (4) Fan; (3) Blog/Radio
Station; (2) Artist/Record label; (1) News Media; (0) unknown. In order to determine the user’s
role, we first looked at the description on the user’s Twitter account. If we could not determine
the answer from the description, we then looked for other clues on the users profiles such as
tweets, links, or photos. If the users did not fall into options 1-3, they were labeled as a fan.
We classified tweets into 3 categories, based on popularity, topic, and consumption. The
options for popularity were: (3) retweeted more than 200 times; (2) retweeted between 100 and

200 times; (1) retweeted between 50 and 100 times; the tweets were not marked if they were
retweeted fewer than 50 times.
We created the options for the topic category using a grounded theory approach, where
the options were created after reading a sample of tweets. The 7 options for topic were: (7)
TomorrowWorld 2015, (6) A couple that got engaged during Bassnectar’s set; (5) Naked festival
goer found in woods 4 days after TomorrowWorld; (4) A song or part of a song played during or
related to TomorrowWorld; (3) Artist who performed at TomorrowWorld; (2) Specific
performance from TomorrowWorld; (1) the event as a whole.
The options for consumption were: (3) Attended TomorrowWorld. This was based off if
the tweet mentioned being at the event, a story about being at the event, or a picture or video
taken at the event; (2) Watching live online; (1) Consuming TomorrowWorld media not during
the festival; (0) Other.
A coding sheet was developed using the 5 listed categories. (See Appendix A) A sample
of tweets was created by randomly selected 10% of tweets from the largest 8 clusters from the
first data set and the largest 9 clusters from the second data set. Three coders separately coded a
portion of the sample.
Network Analysis
The clusters in this topic-network were identified using the Clauset-Newman-Moore
algorithm. All isolates were placed in the same cluster. The density of each cluster was
calculated as the number of existing links between nodes in within a cluster out of the total
possible number of links within the same cluster.

The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm. Vertex
size was dependent on in-degree of the vertex. Edge width, edge width, and edge opacity was
dependent on edge weight. Cluster label was based off of the user with the highest degree in each
cluster. Only the clusters which we coded for content analysis were labeled. Vertex shape was
set to sphere and the vertex with the highest in-degree in the top clusters was set to show the
users image associated with their Twitter account.
Findings
First Data Set
The topic network consists of 8,133 vertices and a total of 14,458 edges. Out of the
14,458 edges, 10,938 are unique and 3,520 are duplicates. In this network, a duplicate edge
represents a retweet. There are 2,368 self-loops, signifying a tweet that does not mention any
user other than the creator.
The modularity of the network is .63, meaning that the clusters within the network are
highly separate from one another. The network contains 400 clusters, with the majority of them
containing a very small number of vertices. For our study, we chose to analyze the 8 largest
clusters, with the exception of Group2, which was a combination of all of the isolates in the
network.
Figure 1: TomorrowWorld Topic Network.

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The user with the highest in-degree in the network is the TomorrowWorld Twitter
account, with an in-degree of 1235. The only other user with an in-degree close to that of
TomorrowWorld is Fascinatingvids, with an in-degree of 1146. Following these two in terms of
in-degree for the entire network are: YouTube with 722; Kudunews with 391; and Edmvine with
317.
The top users in terms of betweenness centrality are the same as in-degree;
TomorrowWorld with 19127083.11, Fascinatingvids with 11479605.99, and YouTube with
8091981.20. However, while TomorrowWorld connects a large number of clusters together,
Fascinatingvids mostly connects users within the G3:FASCINATINGVIDS: cluster. (Figures 2 &

3) Only 2 links exist that connect Fascinatingvids with users from other clusters. Similar to
TomorrowWorld, the YouTube account acts as a bridge not only within its own group, but also
connects G4:YOUTUBE: with a large number of other clusters. In all three cases, a large
majority of the links is directed at the user.
Figure 2: Twitter Topic Network with TomorrowWorld’s edges highlighted.
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Figure 3: Twitter Topic Network with Fascinatingvids’edges highlighted.
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The top user in terms of in-degree within the first 8 clusters excluding
G2:GROUPLESSTWEETS has their image shown on figure 1. Those users and their in-degree
are shown in figure 4.

Figure 4. Highest in-degree and density for clusters 1-8, excluding the groupless tweets.
As expected, the smaller clusters such as G8:STEVEAOKI have higher densities
compared to the larger clusters. While the density different between G1:TOMORROWWORLD
with .002 and G3:FASCINATINGVIDS: with .001 may not seem significant, cluster size must
also be taken into account. The fact that G1:TOMORROWWORLD is twice as dense as
G3:FASCINATINGVIDS despite having nearly twice the amount of vertices is surprising. The
same can be said for G5:EDMVINE and G6:KUDUNEWS.
G1:TOMORROWWORLD is the only cluster that has a substantial amount of
connections with other clusters. The two clusters with the highest amounts of tweets directed at
them from G1:TOMORROWWORLD were G5:EDMVINE with 72 tweets and
G8:STEVEAOKI with 75. G5:EDMVINE and G8:STEVEAOKI were also in the top 3 of most
tweets directed at G1:TOMORROWWORLD, with 98 and 134. G7:NERVOMUSIC is at the top
of that list though, with 173 tweets directed at G1:TOMORROWWORLD.
Cluster User In-degree Cluster Density
G1:TOMORROWWORL
D
TomorrowWorld 1235 .002
G3:FASCINATINGVIDS Fascinatingvids 1146 .001
G4:YOUTUBE YouTube 722 .001
G5:EDMVINE Edmvine 317 .005
G6:KUDUNEWS Kudunews 391 .003
G7:NERVOMUSIC Nervomusic 135 .004
G8:STEVEAOKI Steveaoki 219 .007

Excluding G2:GROUPLESSTWEETS, which was a combination of isolated tweets,
G3:FASCINATINGVIDS is the most isolated cluster. G3:FASCINATINGVIDS has no tweets
directed at another cluster and only has 2 tweets, 1 from G1:TOMORROWWORLD and 1 from
G7:NERVOMUSIC directed towards their cluster.
Since we sampled and coded the tweets by cluster, we will present the results by each
specific cluster. Duplicates of tweets were deleted. We then sampled 10% of the tweets and
vertices. The coding sheet we followed is available as Appendix A. No vertices were coded as
news media.
G1:TOMORROWWORLD
The majority of the most popular tweets were from artists who tweeted a link to a video
of their performance at TomorrowWorld. Dimitri Vegas has 3 tweets that fall into option 2 on our
coding sheet, having been retweeted between 100 and 200 times. However, the only tweet that
was coded as a 3 was not from an artist, but instead a link to a song tweeted by music label
Spinnin’ Records.
Out of the 112 tweets sampled, 27, or 24% were coded as having attended
TomorrowWorld. 5 tweets (4%) were coded as having watched the festival live online and 50
tweets (45%) were labeled as having consumed TomorrowWorld media at a time other than
during the festival.
The topic option with the most tweets was 2, a specific performance, with 38 tweets
(34%). Tweeting about the general event was the next largest option, with 29 tweets (26%). 2

tweets (2%) were about a body being found in the woods 4 days after the event.
G1:TOMORROWWORLD was the only cluster sampled with tweets that fell into that option.
Out of the 119 vertices sampled, 55 (46%) vertices had an unknown location. Out of the
64 known locations, 21 (32%) were from the U.S. while 43 (67%) were from outside the U.S.
77 (83%) of the vertices were labeled as a fan, 7 (6%) as a blog/radio station, and 12
(10%) as an artist.
G3:FASCINATINGVIDS
Every tweet in G3:FASCINATINGVIDS was a retweet of the same video, tweeted by
Fascinatingvids. Therefore, we did not code G3:FASCINATINGVIDS.
G4:YOUTUBE
There were no tweets in G4:YOUTUBE that were retweeted at least 50 times.
Out of the 70 tweets coded, 69 (99%) were classified as consumed not during
TomorrowWorld. The only other tweet was classified under the “other” option.
43 (61%) were tweets about a specific performance and 25 (36%) were about a specific
song.
Out of the 70 vertices that were sampled, 33 (47%) were unknown locations. Out of the
64 (91%) of the vertices were labeled as a fan, 1 (1%) as a blog/radio, and 5 (7%) as an
artist.

G5:EDMVINE
5 out of the 6 tweets that were retweeted enough to be coded for popularity are videos
tweeted by EDMvine of performances at TomorrowWorld. The other tweet was a link to an
interview with multiple artists and was retweeted over 200 times.
Out of the 21 tweets coded, 1 (5%) was marked as having attended TomorrowWorld. 3
(14%) watched live online, 12 (57%) watched another time, and 5 (24%) were marked as other.
3 tweets (14%) were about a specific song, 2 (10%) about an artist, 8 (38%) about a
specific performance, and 6 (29%) about the even as a whole.
Out of the 48 vertices that were sampled, 17 (35%) were unknown locations. Out of the
46 (96%) of the vertices were labeled as a fan, 0 as a blog/radio, and 2 (4%) as an artist.
G6:KUDUNEWS
Every tweet in G6:KUDUNEWS was a retweet of an article tweeted by Kudunews.
Therefore, we did not code G6:KUDUNEWS. All of the users in that cluster were spam twitter
accounts.
G7:NERVOMUSIC
The only popular tweet in G7:NERVOMUSIC was a link to Nervo’s performance,
tweeted by TomorrowWorld and retweeted between 50 and 100 times.

watched live online, 32 (80%) watched another time, and 4 (10%) were marked as other.
1 tweet (3%) was about a couple getting engaged during Bassnectar’s performance, 14
tweets (35%) were about a specific song, 4 (10%) about an artist, 14 (35%) about a specific
performance, and 7 (18%) about the even as a whole.
Out of the 35 vertices that were sampled, 5 (14%) were unknown locations. Out of the 30
known locations, 9 (30%) were from the U.S. while 21 (70%) were from outside the U.S.
artist.
G8:STEVEAOKI
3 out of the 4 popular tweets in G8:STEVEAOKI were by our about DJ Steve Aoki. Aoki
paired up with Bud Light to surprise a fan with a helicopter ride over TomorrowWorld. The other
popular tweet is a link to a Zedd song, tweeted by FilthyDrop.
1 tweets (7%) were about a specific song, 1 (7%) about an artist, 4 (27%) about a specific
performance, and 9 (60%) about the even as a whole.

artist.
Second Data Set
The topic network consists of 7,939 vertices and a total of 13,298 edges. Out of the
13,298 edges, 9,766 are unique and 3,532 are duplicates. In this network, a duplicate edge
represents a retweet. There are 2,229 self-loops, signifying a tweet that does not mention any
user other than the creator.
highly separate from one another. The network contains 408 clusters, with the majority of them
containing a very small number of vertices. For our study, we chose to analyze the 9 largest
clusters, with the exception of Group1, which was a combination of all of the isolates in the
network.
Figure 5: TomorrowWorld Topic Network Data Set #2.

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The user with the highest in-degree in the network is the TomorrowWorld Twitter
account, with an in-degree of 1903. The user with the second highest in-degree is Lifeasaraver
with an in-degree of 715 and the third highest was YouTube with an in-degree of 540.
The top users in terms of betweenness centrality are the same as in-degree;
TomorrowWorld with 24914851.719, Lifeasaraver with 7666570.21, and YouTube with
5773752.177.
The top user in terms of in-degree within the first 8 clusters excluding
G1:Grouplesstweets has their image shown on figure 5. Those users and their in-degree are
shown in figure 6.

Figure 6. Highest in-degree and density for clusters 2-9.
As expected, the smaller clusters such as G9:Tiesto have higher densities compared to the
larger clusters
The highest numbers of cross cluster links was much higher in the second data set than
the first. The highest number of cross cluster links in the first data set was 173. Those links were
from G7:Nervomusic to G1:TomorrowWorld. In the second data set, there are 4 collections of
cross cluster links that have more than 173 links. Those cross cluster links are:
G4:Borgore to G2:TomorrowWorld with 474 links
G6:Edmvine to G2:TomorrowWorld with 234 links
G9:Tiesto to G2:TomorrowWorld with 197 links
G8:Martingarrix to G2:TomorrowWorld with 196 links
Since we sampled and coded the tweets by cluster, we will present the results by each
specific cluster. Duplicates of tweets were deleted. We then sampled 10% of the tweets and
vertices. The coding sheet we followed is available as Appendix A. No vertices were coded as
User In-degree Cluster Density
G2:TomorrowWorld TomorrowWorld 1903 .001
G3:Lifeasaraver Lifeasaraver 715 .001
G4:Borgore Borgore 134 .003
G5:YouTube YouTube 540 .002
G6:Edmvine Edmvine 346 .002
G7:Ravebooty Ravebooty 279 .003
G8:MatinGarrix MartinGarrix 323 .005
G9:Tiesto Tiesto 147 .009

news media. No edges were coded as couple getting engaged or naked festival goer found in the
woods.
G2:TomorrowWorld
G2:TomorrowWorld consisted of many popular tweets. 12 tweets were retweeted
between 50-100 times while 5 were retweeted between 100-200 times. The only artist to have a
popular tweet was Tiesto whose retweet of @DzekoandTorres promoting a Tiesto song was
retweeted between 50-100 times. Tomorrow_LandEs had 5 popular tweets; the most popular
promoting registration for TomorrowWorld 2015 and getting between 100-200 retweets. The rest
of the popular tweets belonged to TomorrowWorld and nearly all promoted ticket sales for
TomorrowWorld 2015.
Out of the 120 tweets coded, 12 (10%) were marked as having attended TomorrowWorld.
0 watched live online, 61 (50%) watched another time, and 46 (38%) were marked as other.
54 tweets (45%) were about TomorrowWorld 2015, 9 (8%) about a specific song, 6 (5%)
about an artist, 18 (15%) about a specific performance, 25 (21%) about the event as a whole, and
7 (6%) were coded as unknown.
artist/label.
G3:Lifeasaraver

G3:Lifeasaraver consisted only of retweets of a link tweeted by the Lifeasaraver Twitter
account.
G4:Borgore
G4:Borgore consisted of 5 tweets that were coded as popular. 3 were retweeted between
50-100 times while the other two were retweeted between 100-200 times. The majority of the
popular tweets were links to performances from TomorrowWorld.
Out of the 48 tweets coded, 1 (2%) were marked as having attended TomorrowWorld. 2
5 tweets (10%) were about TomorrowWorld 2015, 2 (4%) about a specific song, 5 (10%)
about an artist, 14 (29%) about a specific performance, 16 (33%) about the event as a whole, and
6 (13%) were coded as unknown.
48 (80%) of the vertices were labeled as a fan, 1 (2%) as a blog/radio, and1 (18%) as an
artist/label.
G5:YouTube
While G5:YouTube had no tweets with at least 50 retweets, there were a couple popular
songs. A video of Tiesto performing Lion by MOTi and a video of Martin Garrix’s performance
were tweeted multiple times in many different languages such as English, Spanish, and
Portuguese.

Out of the 73 tweets coded, 0 were coded as having attended TomorrowWorld or
watching live online. 71 (97%) consumed TomorrowWorld media after the festival and 2 (3%)
were coded as other.
0 tweets were about TomorrowWorld 2015. 34 (47%) tweets were about a specific song.
0 tweets were about an individual artist. 33 (46%) were about a specific performance. 4 (5%)
were about the event as a whole and 2 (3%) were coded as other.
Out of the 48 vertices coded in G5:YouTube, 11 (23%) had unknown locations. Out of
the 37 known locations, 4 (11%) were inside the U.S. while 33 (89%) were from outside the U.S.
25 (52%) were labeled as a fan, 19 (40%) as an artist/label, and 4 (8%) as unknown. 0
were coded as blog/radio station.
G6: Edmvine
G6:Edmvine was the only cluster with tweets with over 200 retweets. One tweet was a
video promoting TomorrowWorld and the other tweet with over 200 retweets was about
Skrillex’s performance.
Out of the 22 tweets coded in G6:Edmvine, 3 (14%) attended the festival. 0 watched live
online. 10 (45%) consumed TomorrowWorld media at a later time and 9 (41%) were labeled as
other.
4 tweets (18%) were about TomorrowWorld 2015. 3 (14%) were about a song. 0 tweets
were about artists. 6 (27%) were about a specific performance, 8 (31%) were about the overall
event, and 2 (9%) were labeled as other.

Out of the 52 vertices coded in G6:Edmvine, 9 (17%) had unknown locations. Of the 43
known locations, 26 (60%) were inside the U.S. and 17 (40%) were foreign.
51 (98%) were coded as a fan and the other user (2%) was coded as artist/label.
G7:Ravebooty
G7:Ravebooty had 2 popular tweets, both pictures tweeted by the Ravebooty Twitter
account. One was retweeted between 50-100 times and the other between 100-200 times.
Of the 6 tweets coded, 1 (17%) attended, 0 watched online, 4 (67%) consumed media
later, and 1 (17%) was labeled other.
1 tweet (17%) was about TomorrowWorld 2015, 2 (33%) were about a performance and
the other 3 (50%) were about the event as a whole.
Of the 29 vertices coded, 8 (28%) had unknown locations. Of the 21 known locations, 12
(57%) were inside the U.S. and 12 (43%) were outside the U.S.
All 29 users were labeled as fans.
G8:MartinGarrix
All 3 popular tweets in G8:MartinGarrix were videos of Martin Garrix’s performance at
TomorrowWorld. 1 was retweeted between 50-100 times and the other 2 were retweeted between
100-200 times.
Of the 11 tweets coded, 10 (90%) consumed media later and the other 1 (10%) was
labeled other.

1 tweet (10%) was about TomorrowWorld 2015. 3(27%) was about a song, (45%) were
about a performance and the other 2 (18%) were about the event as a whole.
Of the 28 users coded, 3 (11%) had unknown locations. Out of the 25 known locations, 4
(16%) were inside the U.S. and 21 (84%) were foreign.
27 (96%) were labeled as a fan and the other 1 (4%) was unknown.
G9:Tiesto
G9:Tiesto contained 4 tweets that were coded as popular. 2 of those popular tweets were
about Showtek playing ‘Space Jungle’ at TomorrowWorld and the other 2 were about Tiesto.
Of the 14 tweets coded, 12 (86%) consumed media later and the remaining 2 (14%) were
labeled other.
2 (14%) tweets were about TomorrowWorld 2015. 4(29%) were about a song. 1 (7%)
were about an artist. 3 (21%) was about a specific performance. 2 (14%) were about the overall
event and 2 (14%) were labeled other.
Of the 25 vertices coded, 7 (28%) had unknown locations. Of the 18 known locations, 6
(33%) were inside the U.S. and 12 (67%) were foreign.
17 (68%) were fans. 3 (12%) were a radio station/blog. 3 (12%) were an artist/label and
2 (8%) were labeled as other.
Discussion

In both the first and second data sets, the TomorrowWorld Twitter account had the highest in-
degree and betweenness centrality. While the second highest user in those two categories were
different in the two data sets, they are similar because of their cluster were only about a link they
tweeted a link containing TomorrowWorld media that was retweeted a lot.
A big difference between the two data sets is what people were tweeting about. The different
data sets show a moderate shift in focus from this year’s event to next year’s event.
This is most apparent by looking at the most popular tweets in the data sets. Mainly all of the
popular tweets from the first data set were links to videos of performances from
TomorrowWorld. In contrast, nearly all of the popular tweets from the second data set were about
promoting TomorrowWorld 2015 and links to ticket sales.
There is also a different in topics between tweets that weren’t as popular. The first data set
contained tweets about specific events from TomorrowWorld such as a couple getting engaged or
a festival goer found in the woods a couple days after the festival. However, the second data set
did contain any of those tweets.
Both data sets did however contain a very large number tweets about performances from this
year’s festival.
While tweets about consuming TomorrowWorld media at a different time than during the
festival were the vast majority in both data sets, the second data set contains much fewer tweets
about attending or having attended the festival. This also shows the shift in focus from this year’s
festival to next years.

Another difference in who was tweeting is that from the first data set, the number of people
tweeting from outside of the U.S. was much larger than the amount of domestic users, while the
second data set shows the opposite.
ii. Twitter User Network
Our goals/research questions for the Twitter user network research are:
1. What is our role in the TomorrowWorld conversation?
2. Which of our tweets are fostering the most engagement?
Methods
Data
Along with researching online conversation about TomorrowWorld and electronic music,
we created our own Twitter account to join in in the conversation. The account, named
belugaPOD, is associated with the local KingBeluga electronic music label in Athens, Georgia.
77 vertices and 266 edges relating to our @belugaPOD Twitter user network was
collected ranging from September 4, 2014 to December 8, 2014. Also collected was user
statistics (e.g. profile description, # of followers), and tweet statistics (e.g. tweet date, URLs in
tweet). We collected data using NodeXL’s Twitter User importer, which captures tweets and
users sent by a specified user. This created an ego-centric network.
Content Analysis

We went through our 189 tweets and recorded how each tweet did in terms of attracting
the interest and engagement of our followers and network. The tweets were sorted as either
original content, engagement content, or aggregated content.
Network Analysis
size was dependent on the number of followers the user has. Vertex shape was set to sphere and
our belugaPOD account was set to show our image. The accounts that we interact with more are
located closer to our position in the center.
Findings
Figure 7. BelugaPOD Twitter user network.

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The users with the highest number of followers in our ego-centric network are:
1. Davidguetta – 16,084,669 followers
2. Calvinharris – 5,073,572 followers
3. Skrillex – 3,904,383 followers
Out of those three users, belugaPOD interacted with Calvinharris the most, retweeting four of
his tweets.
The users and the amount of edges directed from belugaPOD to the user are the following:
1. Wesdaruler – 34

2. Astro_shaman – 26
3. Edmsauce – 19
4. Kingbeluga – 15
5. Holotropicjuju – 12
6. Tomorrowworld – 9
Out of those 6, 3 of them are artists under the King Beluga label. Those are Wesdaruler,
Astro_shaman, and Holotropicjuju. Edmsauce is a popular blog about electronic music. The
TomorrowWorld music festival rounds out the top 6.
While belugaPOD was not in the first Twitter topic network, our account was more
successful the second time around. BelugaPOD had two tweets in the TomorrowWorld topic
network. Those two tweets were to the Edmsauce and Tomorrowworld accounts. BelugaPOD did
have any edges directed towards it.
The 2 tweets with most engagement were both coded as original content. Those two tweets
were “Come out to @TheGoBar tonight at 9 p.m. to check out @KingBeluga djs
@HolotropicJUJU and @Astro_Shaman kick off @AthensIn” which received 3 retweets and
“I hear if enough people come to the show tonight, @HolotropicJUJU might crowd surf.
Can't confirm or deny source” which received 2 retweets and 1 reply. The only other tweets
that received more than 1 mention were all original content. There were four tweets coded as
aggregated content that received 1 mention and 2 coded as engagement content that received
1 mention.

Discussion
BelugaPOD is not a significant user is the TomorrowWorld topic network. However, the
fact that the account is a part of the second data set is encouraging. In order to become a more
important user in the conversation, we have to continue to tweet about the topic and interact with
more of the important users within the topic. A positive finding is that many of the users in our
user network we have interacted with such as the TomorrowWorld account and Calvin Harris. If
we continue to interact with these important users, hopefully they will begin to recognize our
presence and ideally help us increase our reach.
While we tried to garner interaction on Twitter through engagement questions such as
“Who would you guys like to see replace Avicii at Tomorrowworld?” we were met with little
success. We believe this is because BelugaPOD is not important enough yet on Twitter for most
users to reply to. Our tweets that received the most engagement were Tweets that mentioned
KingBeluga or an artist under the King Beluga label such as Wesdaruler or Astro_Shaman. We
received more engagement on those tweets because these accounts and artists already have a
larger network and following than BelugaPOD and by mentioning them, we are often reaching
their followers.
iii. YouTube
Our goals/research questions for our YouTube research are:
1. What are the most important and popular YouTube videos regarding TomorrowWorld?
2. What are the most important and popular videos about?
Methods

Data
494 Videos and 1,437 YouTube users ranging from September 10, 2012 and November
29, 2014 were captured using the string “TomorrowWorld 2014.” Also collected was video
statistics such as views and rating. We collected data using NodeXL’s YouTube Video importer,
which collects videos that contain “TomorrowWorld 2014” in the title, keywords, description,
categories, or author’s username. This created an undirected topic network about
TomorrowWorld 2014. In this topic network, vertices are represented by videos and edges are
represented by a connection between videos commented on by the same user.
Measurements
Identifying important users
We identified important videos in our network in three ways. The first set of important
users was determined based off degree (number of videos connected by a shared commenter).
Chosen were the highest degrees in the entire network as well as the highest degree in the top 5
clusters. The second set of important users was determined by betweenness centrality (how
important the video is in connecting videos and clusters within the network). Chosen were the
top 5 videos in the entire network. The last set of important users was based of the number of
views. Chosen were the 6 most viewed videos in the network.
Content Analysis
We classified videos into categories based off the year associated with the video, the topic
of the video, and the user who posted the video.

The options for year were (3) 2014, (2) 2013, (1) 2012, and (0) other. In order to
determine the year, we looked at the title, description, and posted date.
The options for topic were (6) a different music festival, (5) a promotional video, (4) an
artist, (3) an aftermovie/thank you/recap, (2) a specific performance, (1) a song, and (0) other.
The options for user were (2) artist/label, (1) a TomorrowWorld/TomorrowLand
associated account, and (0) other.
A coding sheet was developed using the 5 listed categories. (See Appendix B) All videos
in clusters 1-5 were coded. A random sample of 20% of the videos in the group of isolated videos
was coded. Also coded were the 6 top videos in terms of degree, betweenness centrality, and
views. Three coders separately coded a portion of the videos
Network Analysis
The clusters in this topic-network were identified using the Clauset-Newman-Moore
algorithm. All isolates were placed in the same cluster. The density of each cluster was calculated
as the number of existing links between nodes within a cluster out of the total possible number of
links within the same cluster.
size was dependent on degree of the vertex. Group label was based off the user in each group
who uploaded the most videos. Vertex shape was set to sphere.
Findings

This topic network consists of 494 vertices and a total of 1,437 edges. Out of the 1,437
edges, 1,121 are unique and 316 are duplicates. In this network, a duplicate edge signifies that 2
videos have multiple shared commenters.
highly separate from one another. The network contains 29 clusters, with a large number
containing a small number of vertices. For our study, we chose to analyze the 5 largest clusters
and the important users.
Figure 8. TomorrowWorld 2014 YouTube Network.
!
The video with the highest degree in the network is titled, “TomorrowWorld 2014 Nicky
Romero.” That video has a degree of 35. “TomorrowWorld 2014 Yves & Regi” and
“TomorrowWorld 2014 Carnage” were the second highest degree videos with degrees of 34. The

official after movie had a degree of 33 while “TomorrowWorld 2014 Deorro” and
“TomorrowWorld 2014 Steve Aoki” had degrees of 32.
The video with the highest betweenness centrality was also “TomorrowWorld 2014 Nicky
Romero” with 6016.236. Following that video is “TomorrowWorld 2014 Yves & Regi” with
3219.091, “SKRILLEX LIVE @ TomorrowWorld 2014 (Super HD) - [Full Set]” with 2994.612,
“Diplo – Live @ TomorrowWorld 2014 (Saturday) Full Set” with 2965.243 and “Zedd Alive at
TomorrowWorld 2014” with a betweenness centrality of 2568.
The most connected clusters are G1:TomorrowWorld and G3:Traumzauberwald live sets
with 24 connections.
The 6 highest viewed videos are: “Tomorrowland 2012 | official aftermovie” with
119,708,063 views, “Tomorrowland 2013 | official aftermovie” with 84,448,872 views, “Dimitri
Vegas & Like Mike – Live at Tomorrowland 2014 – ( Full Mainstage Set HD )” with 17,015,067
views, “Tomorrowland 2014 | official aftermovie” with 16,591,446 views, “Hardwell Live at
Tomorrowland 2014 [FULL HD]” with 11,018,436 views, and “TomorrowWorld 2014 ft. Zedd
& Steve Aoki | #UpForWhatever | Bud Light” with 7,174,530 views.
The top video in terms of degree in the top 5 clusters, as well as cluster density in shown
in figure 9.
Figure 9. Highest degree and density for YouTube clusters 1-5.
Cluster Video title Degree Cluster density

It is surprising that the density of G4:Primecutpro is lower than G2:DonJulio312 and
G3:Traumzauberwald live set despite being smaller.
Since we sampled and coded the videos by cluster, we will present the results by each
specific cluster. The coding sheet we followed is available as Appendix B.
G1: TomorrowWorld
There were 89 videos coded. The results are the following:
Year
2014 – 73 (82%)
2013 – 15 (17%)
2012 – 1 (1%)
Unknown – 0
Topic
Different Festival – 2 (2%)
Promotional video – 16 (18%)
G1:TomorrowWorld TomorrowWorld 2014
Nicky Romero
35 .142
G2:DonJulio312 Bro Safari at
TomorrowWorld 2014
– All Your Bass – Day
3 – Part 1 of 1
22 .374
G3:Traumzauberwald
live set
Deorro – Live @
TomorrowWorld 2014
(Saturday)
21 .365
G4:Primecutpro TomorrowWorld
Music Festival 2014 –
Saturday
8 .156
G5:Treebeard Zedd Alive at
TomorrowWorld 2014
13 .463

Artist – 4 (4%)
Aftermovie/thank you/recap – 12 (13%)
Specific performance – 37 (42%)
Song – 14 (16%)
Other – 4 (4%)
User
Artist/Label – 14 (16%)
TomorrowWorld/TomorrowLand – 60 (67%)
Other – 15 (17%)
G2:DonJulio312
There were 34 videos coded. The results are the following
Year
2014 – 31 (91%)
2013 – 3 (9%)
2012 – 0
Unknown – 0
Topic
Different Festival – 0
Promotional video – 0
Artist – 0
Song – 2 (6%)
Other – 2 (6%)
User
Artist/Label – 0
TomorrowWorld/TomorrowLand – 0

Other – 34 (100%)
G3:Traumzauberwald live sets
Year
2014 – 29 (100%)
2013 – 0
2012 – 0
Unknown – 0
Topic
Artist – 0
Song – 1 (3%)
Other – 0
User
Artist/Label – 0
Other – 28 (97%)
G4:Primecutpro
Year
2014 – 22 (100%)
2013 – 0
2012 – 0

Unknown – 0
Topic
Promotional video – 2 (9%)
Artist – 0
Song – 4 (18%)
Other – 5 (23%)
User
Other – 21 (95%)
G5:Treebeard
Year
2014 – 17 (100%)
2013 – 0
2012 – 0
Unknown – 0
Topic
Artist – 0
Aftermovie/thank you/recap – 0
Song – 12 (71%)

Other – 0
User
Artist/Label – 0
Other – 17 (100%)
Ungrouped Vertices
There were 43 video coded. The results are the following:
Year
2014 – 42 (98%)
2013 – 0
2012 – 0
Unknown – 1 (2%)
Topic
Artist – 0
Song – 5 (11%)
Other – 2 (4%)
User
Artist/Label – 0
Other – 43 (100%)
Highest Degree
There were 6 video coded. The results are the following:
Year

2014 – 6 (100%)
2013 – 0
2012 – 0
Unknown – 0
Topic
Artist – 0
Song – 0
Other – 0
User
Artist/Label – 0
Other - 0
Highest Betweenness Centrality
Year
2014 – 6 (100%)
2013 – 0
2012 – 0
Unknown – 0
Topic
Artist – 0

Aftermovie/thank you/recap – 0
Song – 0
Other – 0
User
Artist/Label – 0
Other - 0
Highest Views
Year
2014 – 4 (67%)
2013 – 1 (17%)
2012 – 1 (17%)
Unknown – 0
Topic
Artist – 0
Song – 1 (17%)
Other – 0
User
Other – 1 (17%)

Discussion
In terms of importance, we found that Nicky Romero’s full set performance at
TomorrowWorld 2014 was arguably the most important of our YouTube data for having the
highest betweenness centrality (6016.236) and highest degree (35). What makes the video so
important is it connects the most amounts of other similar videos and branches together several
clusters. We concluded from these findings that Nicky Romero’s performance has significant
depth within our data thus proving his importance to our YouTube audience. However, it was
undeniable that the TomorrowWorld 2012 aftermovie was the most popular video of our data that
produced numbers unlike any other link within our network. Having 119,708,063 views
overshadowed any competition for most popular video regarding TomorrowWorld. With such a
high number a views, the TomorrowWorld 2012 aftermovie showed us how globalized and
numerous of an audience we were dealing with.
When looking at our most important and popular videos we found different patterns
pertaining to each video. When looking at the top videos in regards to betweenness centrality we
determined that it was worth noting they were mostly live performances. We concluded that this
is because users who search for one specific live performance are more prone to then watch other
related performances rather than watching one and that’s it. We found similar results when
looking at videos with the highest degree because again it made sense that videos of
performances from TomorrowWorld/TomorrowLand are linked to each other. What was different
was when we looked at videos with the highest views, which were generally the aftermovies.
Aftermovies give a full recap of the entire festival and are renowned for their high quality

footage and production which go viral more easily than said a specific artist’s performance. What
was most peculiar with these findings was how TomorrowLand typically grossed more views
than TomorrowWorld because of its more consistent international audience.
iv. Wikipedia
Our goals/research questions for the Wikipedia research are:
1. What topics are related to TomorrowWorld on Wikipedia?
Methods
Data
2331 Wikipedia pages and 2564 hyperlinks initially stemming from the TomorrowWorld
Wikipedia page were captured. We collected data using NodeXL’s MediaWiki importer, which
captures 2 degrees of hyperlinks stemming from an initial Wikipedia page. This created a
directed egocentric network.
Measurements
We identified important page in our network in three ways: Highest Betweenness
Centrality (how important the page is in connecting other pages and clusters), Highest in-degree
(number of hyperlinks directed at a page), and out-degree (number of hyperlinks directed from a
page). Chosen were the top 5 in each category.
Network Analysis

The clusters in this network were identified using the Clauset-Newman-Moore algorithm.
The density of each cluster was calculated as the number of existing links between pages within
a cluster out of the total possible number on links within the same cluster.
size was dependent on in-degree. Group label was based off our best categorization of the pages
within the cluster.
Findings
The network consists of 2331 vertices and 2564 edges, all of which are unique. The
modularity of the network is .76, meaning that the clusters are very highly separated from one
another. The network contains 9 clusters.
Figure 10. TomorrowWorld Wikipedia Network.

!
The pages with the highest betweenness centrality and their values are the following:
1. Georgia (U.S. state) – 2107951.149
2. Atlanta – 1957087.847
3. Electronic Music – 1858479.95
4. TomorrowWorld – 1601464.266
5. 2013 NCAA Men’s Division I Basketball Tournament – 1400847.277
The pages with the highest in-degree and their values are the following:
1. Atlanta - 6
2. Netherlands - 6
3. Tiesto - 5
4. United States - 5
5. Amsterdam - 5
6. Belgium – 5
The pages with the highest out-degree and their values are the following

1. Georgia (U.S. state) – 581
2. Atlanta – 461
3. Electronic music - 451
4. 2013 NCAA Men’s Division I Basketball Tournament – 331
5. Tiesto – 215
The top user in terms of in-degree in each cluster and cluster density is shown in figure 11.
Figure 11. Highest In-degree and density for each Wikipedia Cluster.
The most connected clusters are Georgia Municipal and Atlanta. There are 37 links going
from the Georgia Municipal cluster to the Atlanta cluster and 34 going in the opposite direction.
Discussion
Cluster Page In-Degree Cluster Density
Georgia Municipal Georgia (U.S. state) 3 .002
Technology Japan 2 .002
Atlanta Atlanta 6 .002
Basketball NCAA Men’s
Division I Basketball
Tournament
3 .003
Foreign EDM Netherlands 6 .005
Countries Armin van Buuren 4 .006
Artists Hardwell 3 .008
TomorrowWorld United States 5 .013
Festivals Guinness World
Record
2 .024

With the exception of the basketball cluster, all of the clusters have a significant
relationship with the TomorrowWorld Wikipedia page. The basketball cluster is certainly the
most surprising cluster in the network. The reason it is included is because the economic impact
of TomorrowWorld was about equal to when Atlanta hosted the NCAA Final Four. The other
clusters highlight important aspects surrounding TomorrowWorld and electronic music.
One of those important aspects is the fact that electronic music is highly global. This is
represented through the Wikipedia network through the large number of foreign countries and
artists located in the Foreign EDM and Countries clusters. It is also represented through the
number of different countries that make up the list of highest in-degrees.
Contrasted with the global aspect of TomorrowWorld is the large number of pages about
the local aspect included in the Georgia Municipal and Atlanta clusters.
More specific clusters such as Festivals and Artists are much denser than broad clusters
such as Technology and Georgia Municipal. As the range of topics within a cluster becomes
smaller, it is more likely that 2 pages within the topic will be connected.
v. Google Analytics
Our goals/research questions for out Google Analytics include:
1. Determine which of our online interactions were most effective in terms of bringing
traffic to our website and keeping users on our website.
Methods

We began our data collection of www.KingBeluga.com using Google Analytics on
Monday, October 6th. Through Google Analytics’Acquisition feature, tracking traffic from
sources such as search engines, referrals via links, emails, and our Facebook and Twitter posts,
we were able to collect and evaluate data about the different traffic brought to our website and
how they engaged with our site. Using the Audience overview feature, we were able to collect
data about the demographics and location of our audience..
Findings
Since beginning to collect data, we have had 193 sessions and 300 page views, from 156
unique users with 81% of visitors being Returning visitors. The averaged Bounce Rate is 74.09%
and average pages/session is 1.55. On average, users remained on the site for 18 seconds.
45.08% of all sessions were users visiting from the United States, 28.50% from Brazil, and
5.18% from Italy. 63.7% of our total traffic came from referrals, which are mostly spam referrals.
However, 7.32% of those referrals were linked from www.soundcloud.com. Our visitors from
Sound Cloud had a Bounce Rate of 33.33%, with an Average Session Duration of 27 seconds,
and 3.22 pages/session. 8.8% of total sessions were from Organic Search, 13.5% from Direct,
and 14% from links provided on our social media pages. Users sent from Facebook and Twitter
had a Bounce Rate of 22.22%, visited an average of 2.11 Pages/Session and remained on the site
for an average of 13 seconds.
Discussion
Over the course of our data collection, we learned that in order to build traffic flow to our
site, it is imperative to be a consistent provider of original content, opinions, and general updates

to fans, giving them as many opportunities to engage with us as possible. Platforms such as
Twitter, Facebook, and Sound Cloud enabled us to connect with intrinsically interested users
who displayed better bounce rates, longer average session times, and a higher ratio of pages
visited per session. We also came to learn that in our initial attempts to connect with a local
demographic, we couldn’t help but attract the attention of international users, many from Italy,
Brazil and other countries. Social Media is an incredibly powerful tool in the promotion and
distribution of Electronic Dance Music, enabling us to link with consumers from around the
world who are interested in many different styles and sub-genres within the culture.
IV. Conclusions
Through our findings, we have come to a couple conclusions about TomorrowWorld. The
first is that TomorrowWorld is a highly global event. The second is that the conversation about
TomorrowWorld goes much deeper than just the event itself, but each artist, performance, and
song fosters its own conversation. The last conclusion is that the TomorrowWorld conversation
timeline extends much earlier and later than just during the festival.
Coming into this project, we originally tried to focus on the local electronic music and
TomorrowWorld scene. However, we quickly learned that localized EDM does not really exist.
Instead, we discovered how highly global TomorrowWorld and electronic music really are. This
was apparent through all of the social media platforms that we analyzed.
As we performed content analysis on our two Twitter Search Network datasets, we were
surprised by the amount of users that were coded as outside of the U.S. In the first data set, 4 out
of 5 clusters that we sampled contained more foreign than domestic users. While the trend

shifted the opposite direction in the second data set, there were still a large number of foreign
users. We also came across of tweets in foreign languages, such as Portuguese and Spanish.
While performing initial research into TomorrowWorld on Twitter, we also came across entire
clusters within the network that consisted of tweets in foreign languages.
Similar to Twitter, there were a substantial number of YouTube videos in which the title
and description were in a language other than English.
When we collected and analyzed the TomorrowWorld Wikipedia network, we found
much of the same. As we performed cluster analysis and attempted to create a cluster name that
best represented the majority of pages in the cluster, we found multiple clusters that had to do
with the global aspect of TomorrowWorld. The Foreign EDM, Countries, and Artists clusters
contained a large number of pages having to deal with foreign countries and people.
Our findings through Google Analytics strengthened our findings through our social
media platforms. Just as a significant portion of the TomorrowWorld conversation was foreign, a
large portion of our website traffic came from outside the United States, specifically from Brazil
and Italy.
Electronic music became popular in Europe and in foreign countries before it gained
popularity in the United States. TomorrowWorld followed that same path. TomorrowWorld is the
American version of Belgium’s TomorrowLand, a fantasy themed festival that has been drawing
huge crowds and audiences since it began in 2005. When festival organizers brought
TomorrowWorld to the United States in 2013, they brought with it a huge international audience.
More than any other music genre, EDM is popular at every corner of the globe. TomorrowWorld

organizers recognized this fact and effectively globalized their brand and captures and worldwide
audience.
We also noticed how many different conversations and niches exist under the
TomorrowWorld blanket term. What makes the TomorrowWorld conversation so complex are
the conversations about specific songs, artists, and performances from the event.
In both Twitter Search Network data sets, the huge majority of tweets were about
specific performances from the festival. In the first data set, two of the most talked about
performances and most important users were Nervo and Steve Aoki, neither of which were main
headliners of the festival. There are plenty of other smaller performances and artists that created
a lot of conversation, such as Borgore from the second data set. More than the overall event, it is
these smaller and more specific conversations that drive the TomorrowWorld conversation.
The YouTube network shows this finding through the important users in the network. Just
like the majority of tweets were about a specific performance, an extremely large portion of
videos in the YouTube network was entire performances or clip from one. However, where
YouTube really emphasizes our finding is through the important users in terms of degree and
betweenness centrality. While the videos with the highest views tended to be aftermovies of the
festivals, those movies were not vital to connecting the network. It is the videos of performances
such as Nicky Romero and Yves & Regi that connect videos throughout the network. This is
because where as an aftermovie may be the only interest of a viewer, viewers who are watching
and commentating on performances are much more likely to be led from one performance to
another, often commenting on both and providing the connections in the network.

Our final conclusion is that while the event only lasts one weekend a year, the
conversation last a lot longer. As we decided on the TomorrowWorld topic and began our
research, we were worried that outside of the festival weekend, there would not be enough
relevant material in the conversation to have significant findings. What we discovered through
research proves that our fear couldn’t be further from the truth.
Both of our data sets we collected were from after the event had already taken place.
However, they tell different stories about the conversation. The first data set reveled a large and
complex conversation about performances and events from this year’s event, initially proving
that our fear was incorrect. Because our second data set was collected about a month after the
first, we assumed that our findings would mirror those from the first set. Instead, we found that a
large portion of the conversation has already shifted towards next year’s event, which isn’t until
September of 2015. Through these findings, it’s safe to assume that Twitter conversation about
TomorrowWorld will be significant throughout the entire year.
While Twitter findings showed us that the conversation persists throughout the year, we
discovered through YouTube that the conversation bridges the different years that
TomorrowWorld has exited. We used the search string “TomorrowWorld 2014” because we did
not want our network to be cluttered with a large number of videos from the past years.
However, even with that search string, we discovered videos in our network pertaining to
previous years of TomorrowWorld. This shows that users do not treat each year’s event as an
isolate, but instead connect each year through a larger conversation.

Electronic music and giant music festivals benefited greatly by gaining popularity around
the same time that social media exploded. Many artists and fans of electronic music are very
active on social media. It is this reason that electronic music has become some a global genre and
generates non-stop conversation, whether it is about the latest festival or the latest song release.
V. Discussion
Based off these findings and conclusions, we have created suggestions of how we can
become more involved in our topic network as well as increase our social media presence and
web traffic.
There are multiple improvements we can make in order to increase our presence in the
TomorrowWorld topic network. The first is that we need to interact with more important users
and more often. We have found out who the important users are connecting people throughout
the network. While we have interacted with some of the users such as the TomorrowWorld
account and artists such as Calvin Harris, we need to interact with more. We also need to interact
with these users on a regular basis so they might eventually recognize us and ideally help us
expand our presence through replies and retweets.
The second improvement we can make to increase our presence in the topic network is
becoming more involved in the smaller conversations. It is not enough to just tweet about the
event as a whole or just with the top users throughout the entire network. Instead we most join
the conversations about specific performances, artists, and songs as well as interact with the most
important users within the individual clusters. We also need to become active on YouTube by

commenting on videos of performances. The only way we will increase our presence in the entire
network is by increase our presence in the smaller sub-networks first.
In order to expand our social media presence, our first suggestion is that we need to
expand our focus from local to global. Despite our account being associated with a local music
label, it is apparent that must interact with a global audience. In order to increase our reach, it is
important to interact with important users and blogs outside of Athens and Georgia, such as
EDM Sauce, a highly regarded international EDM blog, it is also important not to ignore a user
just because he is not in your network or local area. It would also be beneficial to have
knowledge of languages other than English so we are able to interact with a larger online
audience.
While our social media venture hasn’t been wildly successful, we have enjoyed success
in a couple areas. Our final suggestion is to increase the volume of the posts and content that has
proven to have some success.
Our most successful tweets have been original content in which we posted some kind of
media such as a picture, video, or link to audio. We have also been successful when we interacted
with more established users such as the artists under our music label or local music venues. Until
we increase our online presence to a point where we have a significant audience, it is vital that
we use these connections and resources we were given to benefit ourselves.
Finally, it is apparent that our most important web traffic is from Sound Cloud. While not
included in our social media research, Sound Cloud is an important social media platform for
music. Sound Cloud allows people to listen to, like, comment on, and share to new music. While

the number of views on our website from Sound Cloud was small, they were inarguably our most
successful visits, based off bounce rate and page views. We need to greatly increase our Sound
Cloud presence, in terms of liking and commenting on videos, but also posting content such as
our artists music on the platform. Sound Cloud has made it very easy to share songs via Twitter,
a feature that we most capitalize on in order to become successful.
Thankfully, when we created our Twitter account, we were already a part of a network
surrounded by artists and the music label. We must make the most of that opportunity by using
the established network to increase our presence until we have a larger following.
VI. Appendix
A. Twitter Coding Sheet
For our research, we coded tweets and users on a variety of different topics. We created
categories and options for coding based off our prior knowledge of the topic. We then
improved our categories and options by reading through a sample of tweets and creating
additional categories we found necessary based off what we were reading.
The categories for coding the tweets are the following:
Consumption: How did the user consume the material related to the tweet? Some tweets
were ambiguous so we coded them with the possible options. (ex. 3,1)

3: Attendee – was the material in the tweet related to attending TomorrowWorld.
Based off if the tweet mentions being at the event, a story about being at the event,
or a picture/video taken at the event?
2: Watching live on computer – was the material in the tweet related to watching a
live stream of TomorrowWorld?
1: Consuming TomorrowWorld media not during the festival – was the material in the
tweet related to watching/listening/consuming material from or about TomorrowWorld
at a different time than during the festival?
0: Other – the material in the tweet did not fall into any of these categories.
Topic: What was the tweet about?
7: TomorrowWorld 2015
6: A couple that got engaged during Bassnectar’s set
5: naked festival goer found in the woods.
4: song – was the material in the tweet about a particular song or part of a song played
during or related to TomorrowWorld?
3: artist – was the material in the tweet about an artist who performed at
TomorrowWorld?
2: specific performance – was the material in the tweet about a specific performance
at TomorrowWorld?
1: overall event – was the material in the tweet related to the event but not any of
the categories mentioned above?
0: Other
Popularity: How many times was the tweet retweeted?
3: more than 200 times
2: between 100 and 200 times
1: between 50 and 100 times
Not marked: fewer than 50 times
The categories for coding the users are the following:
Producer: Who is the person that sent the message? What role does the user play in the
TomorrowWorld or EDM scene? In order to find this out, we first looked at the description on
the user’s Twitter account. If we could not determine the answer from that, we then looked
for other clues on the users profiles such as tweets, links, or photos. If the user did not fall
into options 1-3, they were labeled as a fan.
4: fan
3: blog/radio
2: artist/label

1: news media
0: unknown
Location: Is the user from the U.S. or outside the U.S.? In order to determine this, we first
looked at the location associated with the user’s profile. We also took into account the
language used by the users.
2: Inside the U.S.
1: Outside of the U.S.
0: Unknown.
B. YouTube Coding Sheet
Year: What year is the video associated with? Based off title, description, and posted date.
3: 2014
2: 2013
1: 2012
0: unknown
Topic: What is the video about?
6: Different festival
5: promotional video
4: artist
3: Aftermovie/Thank You/Recap
2: Specific performance
1: Song
0: Other
Producer
2: Artist/label
1: TomorrowWorld/TomorrowLand
0: Other
G1: TomorrowWorld

G2: DonJulio312
G3: Traumzauberwald live sets
G4: Primecutpro
G5: Treebeard

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