Online Firestorms and the Case of Copenhagen Zoo's Marius the Giraffe - Computational Social Science Lab (CSSL) at Copenhagen Business School

CABS 2014 – Kyoto, Japan

Marius, the Giraffe: A Comparative Informatics Case Study

of Linguistic Features of the Social Media Discourse

Chris Zimmerman@socialbeit

Ravi Vatrapu, Yuran Chen, Dan HardtComputational Social Science Laboratory (CSSL)

Copenhagen Business School, Denmark


Tweets


Facebook Posts

Overview

1. The Copenhagen Zoo Story2. Research Questions3. Datasets and Tools 4. Conversation Composition (Geographic & Media Channel)5. Social Graph: Actors, Posts, Demographics6. Conversation Evolution7. Social Text

1. Sentiment Analysis 2. Danish / English Comparison 3. Language Analysis

8. Social Media Outcomes9. Findings by Channel and Language


Language Analysis

Comparing the Conversation Footprint


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


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?


Timeline of Events

tinyurl.com/mariustimeline

http://www.tinyurl.com/mariustimeline

http://cdn.knightlab.com/libs/timeline/latest/embed/index.html?source=0AmIFsZ0RXdIcdHhDOXZyQ05tNHU0SkVVcmgzS1J4U0E&font=Bevan-PotanoSans&maptype=toner&lang=en&height=650


ToolsetTool Purpose Access

Radian6 Collection License

Nitrogram Collection LicenseTableau Desktop

Visualization / Analysis

License (edu)

Datawrapper Visualization Public

TimelineJS Visualization Public

LIWC Language Analysis Public

SODATOCollection / 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


The Dataset

Queries


#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


Visual Analysis & Full Dataset

tinyurl.com/mariusvisual

http://www.tinyurl.com/mariusvisual

http://public.tableausoftware.com/profile/datavist%23/


Conversation Composition

Geographic and Media Channel Breakdown


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?


Channel Distribution


Distribution (DK)


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%).

Source: Radian6


Social Graph

Who is taking part in the conversation, as detected on

Twitter


Amplified Posts

Source: Twitter, NodeXL, Radian6


#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

Source: Twitter, Twtrland


A Sentimental Topic

Sources : Twitter, Sentiment140,


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?

http://public.tableausoftware.com/views/DanishSentimentDashboardforMarius/SentimentDashboardDK?:embed=y%231

http://public.tableausoftware.com/shared/6CK547CX6?:display_count=no


Conversation Volume

Global Conversation over Time


Volume & Sentiment Over Time


Isolating the Escalation


Week of Activity by Channel


Re-kindling Emotions


Sentiment Timeline


Social Text

SentimentGlobal Dataset

Automatic Sentiment Analysis


Sentiment Analysis by Channel


Sentiment Analysis



Sentiment Analysis


Subjective Conversation


Comparing Languages

Facebook Danish vs the Global English

Conversation


Facebook Fanpage


Affinity & Commentary

Danish Facebook Sentiment

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

Manual Coding Trained System

Pre-Coding



English vs. Danish Manual Coding

DANISH

Facebook Subjectivity

Negative Polarity

(PN Ratio)English 2.16 11.81Danish 1.29 2.32

ENGLISH*


Social Text

LanguageGlobal Dataset

Language Analysis


Word Frequency

• Comparing the frequency of unigrams (single words), bigrams (two words) and trigrams (three words)


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’ ‘@’ )


English Unigrams


English UnigramsBoth 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)


Danish Unigrams


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.


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)


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.


Social Media Outcomes

A Global Performance


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.


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


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

PTAT

Fans

Interactions (LCS)

SOCIAL SET ANALYSIS: COPENHAGEN ZOO GIRAFFE EUTHANASIA CRISIS

55

Distribution of Likes on Copenhagen Zoo’s Facebook Posts

Artefacts: Copenhagen Zoo Facebook PostsActors: Facebook users on Copenhagen Zoo PageActions: LIKE events

Activity: Positive AssociationSociological ImportanceOrganizational 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


Global & Local FindingsHow 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.


Channel FindingsOverall• 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).


Examining Emotions

When


With Whom


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Online Firestorms and the Case of Copenhagen Zoo's Marius the Giraffe - Computational Social Science Lab (CSSL) at Copenhagen Business School