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Project #Marius Chris Zimmerman
Ravi Vatrapu
Yuran Chen
Dan Hardt
Real World Reflections
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
Scoailbakers Facebook Statistics COTS*
Followerwonk Context COTS*
Twtrland Context COTS*
Quintly Context COTS*
Wildfire Historical Performance COTS*
Consumer of the shelf tool (COTS)
#Marius Overview
Social Data Collected
• 40 Online Channels (Jan 19 – Feb 19)
• Over 315 K Posts Collected (75% Twitter)
• 200 K Unique Posts (63%)
• 681 Million Potential Impressions on Twitter
• Highest Buzz Rate : 332 Posts / Minute
Normal Monthly Volume : 300-500 Stories
• 30K Petition Signatures
• 45K Facebook Protesters
Visual Analysis & Full Dataset
(click here)
Research Avenues of Inquiry
The online reflection - Why does this matter?
• Volume – How did the conversation amplitude evolve over two weeks in February online?
• Sentiment – Where did negative sentiment originate and how did it evolve/spread? (keywords, people, and topics)
• Community – Who were the relevant actors? (Organizations, Customers, Users, Activists, Influencers, etc.)
• Post-level Performance – What types of posts and specific events instigated the issue online? (artifacts involved such as videos, photos, Facebook posts, tweets, etc)
How did the Copenhagen Zoo handle the event on social channels and how did the (social) media storm effect their presence? How did other organizations deal with the crisis? What made this incident different and how?
Dataset
Distribution
Distribution (DK)
Channel Comparison • Twitter dominates 75% of total
chatter, while 21% is from Facebook Discussions
• Amplification: 50% of Tweets are retweets
Danish Subset
• Media channels are more rich in diversity
• Facebook and Twitter only share half the conversation
• 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?
Region and Language
Detection
• 95% of the total
conversation was
detected to be in English.
• Almost two thirds of
global activity came from
the US (64%), followed by the UK (13%) and
Netherlands (4%).
• Danish was only detected
in 2,220 posts.
Timeline of Events
(click here)
Social Text
A Sentimental Topic
Radian6
Sentiment-challenged Examples:
Automatic Sentiment Results
• 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?
Language Comparison
#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
Network Analysis
Amplification Influentials
#Marius @CopenhagenZoo
Amplified Posts
Centrality
Vertex In-Degree
Out-Degree
Betweenness Centrality
Closeness Centrality
Eigenvector Centrality PageRank
Clustering Coefficient Custom Menu Item Action
Tweeted Search Term?
copenhagenzoo 513 0 767401.779 0.001 0.038 122.346 0.002 https://twitter.com/copenhagenzoo No
digitalcake2 1 62 89745.114 0.000 0.004 17.351 0.009 https://twitter.com/digitalcake2 Yes
aprilchristen 27 5 41415.806 0.000 0.001 8.279 0.016 https://twitter.com/aprilchristen Yes
beaumiroir 14 7 39865.026 0.000 0.001 5.353 0.023 https://twitter.com/beaumiroir Yes
01bond 38 1 32366.167 0.000 0.000 9.443 0.000 https://twitter.com/01bond Yes
rtenews 37 0 28708.167 0.000 0.000 8.838 0.000 https://twitter.com/rtenews No
ebizniz 1 9 23755.440 0.000 0.000 3.216 0.000 https://twitter.com/ebizniz Yes
mmpr_consultant 17 6 19567.043 0.000 0.003 4.570 0.082 https://twitter.com/mmpr_consultant Yes
earthtransition 12 1 19429.063 0.000 0.000 4.656 0.006 https://twitter.com/earthtransition Yes
Copenhagen Zoo Facebook
Performance
• Largest surge in likes ever
• Almost 100K People Talking About This (PTAT) on Facebook
• 120.3% Normalized Buzz (PTAT/Likes)
Global Fan Growth
• Over 10K new fans this 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.
Likes
PTAT
PTAT / Likes
Interactions (LCS Historical)
Check-ins
• Beforehand, 29K
people added the
Zoo’s location to a
Facebook post
• Now 110K people
“Were Here” on
• CPH Zoo is thus now
the 7th most checked-
into place in Denmark
Initial Findings
Overall
• Twitter offered a more direct reflection of events, in terms of volume and sentiment
• Twitter also demonstrated a more drastic reaction to network prestige factors from activists and celebreties
• Automatic sentiment on Radian6 is neutral-heavy, often failing to detect negative sentiment,
• 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).
Danish Comparison
Contrast with Danish Subset
• Mainstream media plays a larger role as opposed to higher proportions of online debate on social channels elsewhere
• Re-tweets levels are relatively small and social media may be used social media more to express oneself rather than to share information.
• Negative sentiment was detected more strongly on Facebook
