Post on 21-Jun-2015
description
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
CABS 2014 – Kyoto, Japan
Tweets
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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
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Language Analysis
Comparing the Conversation Footprint
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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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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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Timeline of Events
tinyurl.com/mariustimeline
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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
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The Dataset
Queries
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#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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Visual Analysis & Full Dataset
tinyurl.com/mariusvisual
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Conversation Composition
Geographic and Media Channel Breakdown
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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?
CABS 2014 – Kyoto, Japan
Channel Distribution
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Distribution (DK)
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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
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Social Graph
Who is taking part in the conversation, as detected on
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Amplified Posts
Source: Twitter, NodeXL, Radian6
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#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
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A Sentimental Topic
Sources : Twitter, Sentiment140,
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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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Conversation Volume
Global Conversation over Time
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Volume & Sentiment Over Time
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Isolating the Escalation
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Week of Activity by Channel
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Re-kindling Emotions
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Sentiment Timeline
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Social Text
SentimentGlobal Dataset
Automatic Sentiment Analysis
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Sentiment Analysis by Channel
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Sentiment Analysis
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Sentiment Analysis
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Subjective Conversation
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Comparing Languages
Facebook Danish vs the Global English
Conversation
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Facebook Fanpage
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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
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English vs. Danish Manual Coding
DANISH
Facebook Subjectivity
Negative Polarity
(PN Ratio)English 2.16 11.81Danish 1.29 2.32
ENGLISH*
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Social Text
LanguageGlobal Dataset
Language Analysis
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Word Frequency
• Comparing the frequency of unigrams (single words), bigrams (two words) and trigrams (three words)
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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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English Unigrams
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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)
CABS 2014 – Kyoto, Japan
Danish Unigrams
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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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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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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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Social Media Outcomes
A Global Performance
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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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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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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
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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.
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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).
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Examining Emotions
When
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With Whom
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