This document presents a case study on the social media discourse surrounding the euthanization of a giraffe named Marius at the Copenhagen Zoo. It analyzes datasets from Twitter and Facebook to examine differences between the Danish and international conversations. Key findings include that the international discussion was larger in volume and more negative in sentiment, while the Danish conversation was more neutral and occurred more on Facebook than Twitter. Differences in word frequencies between the Danish and English texts provide clues about advocacy hashtags, descriptions of what occurred, and factors that increased virality of the story internationally.

1. Marius, the Giraffe:
A Comparative Informatics Case Study
of Linguistic Features of the Social Media Discourse
Chris Zimmerman
@socialbeit
Ravi Vatrapu, Yuran Chen, Dan Hardt
Computational Social Science Laboratory (CSSL)
Copenhagen Business School, Denmark
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2. Tweets
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3. Facebook Posts
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4. Overview
1. The Copenhagen Zoo Story
2. Research Questions
3. Datasets and Tools
4. Conversation Composition (Geographic & Media Channel)
5. Social Graph: Actors, Posts, Demographics
6. Conversation Evolution
7. Social Text
1. Sentiment Analysis
2. Danish / English Comparison
3. Language Analysis
8. Social Media Outcomes
9. Findings by Channel and Language

5. Language Analysis
Comparing the Conversation Footprint
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6. Sentiment Shifting
Sentiment Analysis
• POS – consistency dissipates
• NEU – more even distribution
• NEG – isolated, intense levels
Overall shift in conversation sentiment
• Longer lasting effect?
9am-5pm
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7. Research Questions
1.What specific differences are there in the
Danish vs. international interactions?
2.Can these differences be traced to features
of the media landscape in Denmark?
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8. Timeline of Events
tinyurl.com/mariustimeline
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9. Toolset
Tool Purpose Access
Radian6 Collection License
Nitrogram Collection License
Tableau Desktop Visualization / Analysis License (edu)
Datawrapper Visualization Public
TimelineJS Visualization Public
LIWC Language Analysis Public
SODATO Collection / Vizualization Beta
Topsy Pro Collection / Analysis Trial
Socailbakers Facebook Statistics COTS*
Followerwonk Context COTS*
Twtrland Context COTS*
Quintly Context COTS*
Wildfire Historical Performance COTS*
Consumer of the shelf tool (COTS)
30 days of data collection
13 Social Data Science Tools
50 Corporate
Communications Students
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10. The Dataset
Queries
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11. #Marius Overview
Social Data Collected
• 40 Online Channels (Jan 19 – Feb 19)
• Over 315 K Posts Collected*
• 200 K Unique Posts (63%)
• 681 Million Potential Impressions on Twitter
• 332 Posts/Minute – Peak Buzz Rate
• 75% Twitter
• 45K Facebook Protesters on FB “Save Marius”
Pages
• 30K Online Petition Signatures
*Normal Monthly Volume : 300-500 Stories
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12. Visual Analysis & Full Dataset
tinyurl.com/mariusvisual
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13. Conversation Composition
Geographic and Media
Channel Breakdown
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14. Channel Comparison
• Twitter dominates 75% of total chatter,
while 21% is from Facebook Discussions
• Amplification: 50% of Tweets are
retweets
Danish Dataset
• Facebook and Twitter only share half the
conversation
• Media channels are more rich in
diversity
• Only a quarter of all Tweets are re-tweets
Does mainstream media play a greater role
for Danish society while, social media is
dominant elsewhere in terms of quantity of
discussion and breadth of dispersion?
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15. Channel Distribution
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16. Distribution (DK)
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17. Region & Language Detection
• 95% of the total
conversation was
detected to be in English.
• 4,023 German Posts
• Danish was only detected
in 2,220 posts.
• Almost two thirds of
global activity came from
the US (64%), followed by
the UK (13%) and
Netherlands (4%).
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Source: Radian6

18. Social Graph
Who is taking part in the
conversation, as detected on
Twitter
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19. Amplified Posts
Source: Twitter, NodeXL, Radian6
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20. #Marius Demographics
Location estimates
North America
usage over 50%
Twitter bio field
reveals several
dominant traits
during the weekend:
• Liberal,
• Progressivism,
• Vegan,
• Activist,
• Animal rights,
• advocate, pets,
wildlife, etc
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Source: Twitter, Twtrland

21. A Sentimental Topic
Sources : Twitter, Sentiment140,
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22. Sentiment Pre-Detection
• Danish data tends to be
much more neutral
compared to the non-
Danish data.
• Most of the negativity
detected in Twitter for
non-Danish data while
most of the negative
data occurs in
Facebook for Danish
data.
> Does this imply that
Danes prefer Facebook to
Twitter to express their
ideas?
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23. Conversation Volume
Global Conversation over Time
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24. Volume & Sentiment Over Time
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25. Isolating the Escalation
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26. Week of Activity by Channel
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27. Re-kindling Emotions
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28. Sentiment Timeline
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29. Social Text
Sentiment
Global Dataset
Automatic Sentiment Analysis
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30. Sentiment Analysis by Channel
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31. Sentiment Analysis
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32. CABS 2014 – Kyoto, Japan

33. Sentiment Analysis
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34. Subjective Conversation
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35. Comparing Languages
Facebook
Danish vs the Global English
Conversation
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36. Facebook Fanpage
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37. Affinity & Commentary
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38. Danish Facebook Sentiment
Manual Coding Trained System
Facebook-
DK %Neg %Neu %Pos PN Ratio Subj.
Manual
Coding
39% 44% 17% 1.29
Trained
System
49% 21% 30% 3.67
Radian6 10% 89% 1% 0.13 0.13
Pre-Coding
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39. English vs. Danish
Manual Coding
DANISH
Facebook Subjectivity
Negative
Polarity
(PN Ratio)
English 2.16 11.81
Danish 1.29 2.32
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ENGLISH*

40. Social Text
Language
Global Dataset
Language Analysis
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41. Word Frequency
• Comparing the frequency of unigrams (single words),
bigrams (two words) and trigrams (three words)
Rank
1
Counts
3296
Word
,
2 1875 /
3 1737 zoo
4 1475 marius
5 1354 :
6 1304 giraf
7 1056 #
8 926 københavn
9 671 !
10 521 http
11 506 københavns
12 503 ?
13 488 t
14 480 co
15 414 giraffen
16 412 bengt
17 402 holst
18 377 så
19 330 -
20 321 skriv
21 321 ledende
22 320 stilling
23 320 holstzooshame
24 320 fjernes
25 307 kan
26 305 søndag
27 266 løverne
28 229 se
29 214 død
30 207 @
31 202 dyr
32 188 fodres
33 176 aflivede
34 175 fordi
35 173 kl
36 171 10
37 170 formiddag
38 166 skudt
39 159 zoologiske
40 159 ved
41 156 aflivet
42 151 boltpistol
43 146 gang
44 145 rt
45 143 2-årige
46 139 to
47 137 dk
48 136 gik
49 136 (
50 135 aflivningen
51 133 )
52 130 obduktionen
53 129 parteres
54 128 andre
55 126 billederne
56 124 går
57 122 |
58 118 verden
59 116 2014
60 115 aflives
61 112 the
62 109 unge
63 105 bare
64 103 lige
65 100 løvefoder
66 98 livet
67 97 danmark
68 96 park
69 96 helt
70 95 aflivning
71 94 korte
72 93 zoologisk
73 93 børn
74 93 avis
75 91 år
76 91 hele
77 87 blevet
78 87
79 86 mere
80 86 godt
81 85 zoos
82 84 folk
83 83 ung
84 83 copenhagen
85 79 samme
86 77 udløst
87 76 s
88 75 medier
89 73 navn
90 73 heftig
91 72 indlæg
92 72 få
93 72 diskussion
94 70 orden
95 70 facebook
96 68 heller
97 68 februar
98 67 spørgsmål
99 66 dagens
100 64 aflive
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42. Most Frequent
• Pointing Activity: Traces of links were the most
frequently used characters suggesting high degree of
‘Pointing Activity’
• Emotion or Inquiry: Exclamation points and question
marks appeared in the top 20 most used unigrams.
• Conversational Nature: The term RT (12th most frequent)
shows Twitter amplification (people echoing sentiments
they agree with), while use of the @ sign signals a high
level of directed communication on Twitter.
( ‘/’ ‘:’ )
( ‘!’ ‘?’ )
( ‘RT’ ‘@’ )
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43. English
Unigrams
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44. English Unigrams
Both English & Danish had a high degree of giraffe
description unigrams
However ‘Killing Words’ appear with high frequency,
preceding clinical terms ‘euthanize’
• Danish variants of “euthanize” (aflivede) occur at 33,
41, and 60, much higher than the more negative
variants of “killing” in Danish (dræb at 349, 552, 769
and 778).
Intensely negative “Death Verbs” in English
• Corresponding words also appear in Danish, however
with less frequency, such as butcher (slagte - 271) or
murderers (mordere - 298)
healthy (15)
young (22)
baby (39)
beautiful (96)
killed (16)
killing (20)
kill (31)
kills (45)
“destroying” (91)
“murdered” (108)
“slaughtered” (112)
“butchered” (136)
“slaughter” (138)
“execution” (14)
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45. Danish
Unigrams
Rank
1
Counts
3296
Word
,
2 1875 /
3 1737 zoo
4 1475 marius
5 1354 :
6 1304 giraf
7 1056 #
8 926 københavn
9 671 !
10 521 http
11 506 københavns
12 503 ?
13 488 t
14 480 co
15 414 giraffen
16 412 bengt
17 402 holst
18 377 så
19 330 -
20 321 skriv
21 321 ledende
22 320 stilling
23 320 holstzooshame
24 320 fjernes
25 307 kan
26 305 søndag
27 266 løverne
28 229 se
29 214 død
30 207 @
31 202 dyr
32 188 fodres
33 176 aflivede
35 173 kl
36 171 10
37 170 formiddag
38 166 skudt
39 159 zoologiske
40 159 ved
41 156 aflivet
42 151 boltpistol
43 146 gang
44 145 rt
45 143 2-årige
46 139 to
47 137 dk
48 136 gik
49 136 (
50 135 aflivningen
51 133 )
52 130 obduktionen
53 129 parteres
54 128 andre
55 126 billederne
56 124 går
57 122 |
58 118 verden
59 116 2014
60 115 aflives
61 112 the
62 109 unge
63 105 bare
64 103 lige
65 100 løvefoder
66 98 livet
67 97 danmark
68 96 park
69 96 helt
70 95 aflivning
71 94 korte
72 93 zoologisk
73 93 børn
74 93 avis
75 91 år
76 91 hele
77 87 blevet
78 87
79 86 mere
80 86 godt
81 85 zoos
82 84 folk
83 83 ung
84 83 copenhagen
85 79 samme
86 77 udløst
87 76 s
88 75 medier
89 73 navn
90 73 heftig
91 72 indlæg
92 72 få
93 72 diskussion
94 70 orden
95 70 facebook
96 68 heller
97 68 februar
98 67 spørgsmål
99 66 dagens
100 64 aflive
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46. Danish Unigrams
• Objective or Descriptive words around what happened at the
zoo in question.
– Referring to the time of public autopsy, the number 10 and “kl”
(o’clock) appear at 34th and 35th position in frequency.
– And the word “shot” (skud-38), “boltgun” (boltpistol-42) and
“dissect” (parteres-53) all appear higher in Danish than in English.
• Explanation Discourse: high frequency of “fordi” (because).
– This term does not appear among the high-frequency English
words, where the only highly frequent discourse particle is
“despite”.
• Advocacy #holstzooshame
– While The advocacy hashtag appeared with similar frequency in
both languages (23/26)*
– DEN - ‘Bengt’ ‘Holst’ names appear extremely high individually in
Danish (at 16 and 17), but perhaps not negatively.
– ENG - ‘petition’ (38) and ‘sign’ (53) are prominent examples of
activism activity in English only.
*Bengt Holst is the scientific director, who as a figurehead took much of the blame/credit for how the zoo
handled the event.
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47. Topicality: Clues for Virality
Medium / Transparency Differences
• Photographable: The openness of
the event itself and the use of
graphic photos could reinforce the
emotional weight that user posts
carry on social channels.
– “photo” appears much higher in
English than in Danish (42 /141).
• Public Display: A point of public
outrage was the fact that in the
photos people saw online, children
were allowed and encouraged to
witness the scientific autopsy of the
animal.
– “children” (40) appears higher in the
English data than in Danish (børn-73)
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48. Virality Clues (cont.)
An Animal With a Name
• #Marius hashtag helped to categorize and launch
the conversation worldwide
• The fact that the giraffe had a name, Marius,
increased the attachment ability when compared
to other controversial human incidents with animals.
– “named” unigram at 33 in English and “navn” at
89 in Danish.
– “giraffe named marius” is also one of the very
most frequent trigrams (11) with 15,225
occurrences in English.
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49. Social Media Outcomes
A Global Performance
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50. Community
Global Fan Growth
• Over 10K total new fans that month (70% in Denmark)
• 19 Countries more than doubled their fanbase
• Countries such as the UK and Australia tripled and almost quadrupled
their fanbases of CPH Zoo.
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51. Check-ins on Location
• Beforehand, 29K
people added the
Zoo’s location to a
Facebook post
• After the Giraffe, 110K
people “Were Here”
on Facebook
• CPH Zoo is thus now
the 7th most checked-into
place in Denmark
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52. Copenhagen Zoo Facebook Page
• 68% of all historical page
interactions occurred in
the month of February
2014
• The largest surge in likes
ever
• Almost 100K People Talking
About This (PTAT) on
Facebook
Interactions (LCS)
PTAT
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Fans

53. SOCIAL SET ANALYSIS: COPENHAGEN ZOO
GIRAFFE EUTHANASIA CRISIS
Distribution of Likes on Copenhagen Zoo’s Facebook Posts
Artefacts: Copenhagen Zoo Facebook Posts
Actors: Facebook users on Copenhagen Zoo Page
Actions: LIKE events
55
Activity: Positive Association
Sociological Importance
Organizational Relevance
LIKES During Crisis:
LIKEs were a way of expressing solidarity and
support to a Danish institution perceived to be
under undeserved criticism.
During Crisis (2-weeks)
05-19 February, 2014

54. Global & Local Findings
How Danish differs from English
DENMARK(how conversation contrasts with ENG dataset)
• Mainstream media plays a larger role as opposed to higher
proportions of online debate on social channels elsewhere
• Re-tweet levels are relatively small and social media may be
used more to express oneself individually rather than to share
information.
Social Text Findings
• Negative sentiment was detected more strongly on
Facebook.
• Subjectivity was less in Denmark, neutral posts most frequent.
• Polarity was also more balanced, even with significant
positivity levels.
• Language analysis revealed differences in reaction intensity
between languages, with far more polarized word usage in
English.
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55. Channel Findings
Overall
• Twitter offered a more direct reflection of events, in terms of volume
and sentiment
• Facebook contained greater subjective discourse during a
controversy that evolved as a social media firestorm
• Twitter, and in particular retweets, contained the highest degrees of
negative polarity.
Considerations
• The dominance of English-language countries and the Twitter
channel went hand-in-hand (perhaps along with mainstream spin).
• The mechanisms on Facebook allow a dichotomy from crisis
situations by yielding negative sentiment in terms of comments and
posts, while simultaneously experiencing unprecedented growth in
positive signals (such as fans and likes, as well as buzz and check-ins).
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56. Examining Emotions
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57. When

58. With Whom
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59. @socialbeit
cz.itm@cbs.dk
cssl.cbs.dk
follow our projects
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