Project #Marius
Chris Zimmerman
Ravi Vatrapu
Yuran Chen
Dan Hardt
Social Business Intelligence
Real World Reflections
Toolset
Tool

Purpose

Access

Radian6

Collection

License

Nitrogram

Collection

License

Tableau Desktop Visualization...
#Marius Overview
Social Data Collected
• 40 Online Channels (Jan 19 – Feb 19)
• Over 315 K Posts Collected (75% Twitter)
•...
Visual Analysis & Full Dataset

(click here)
Research Avenues of Inquiry
The online reflection - Why does this matter?
• Volume – How did the conversation amplitude ev...
Dataset
Distribution
Distribution (DK)
Channel Comparison
•
•

Twitter dominates 75% of total
chatter, while 21% is from Facebook
Discussions
Amplification: 50% ...
Region and Language
Detection
• 95% of the total
conversation was
detected to be in English.
• Almost two thirds of
global...
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 nonDanish data.
• Most of the nega...
Language Comparison
#Marius Demographics
Location estimates
North America
usage over 50%
Twitter bio field
reveals several
dominant traits
dur...
Network Analysis
Amplification Influentials

#Marius

@CopenhagenZoo
Amplified Posts
Centrality

Vertex
copenhagenzoo

InOutBetweenness Closeness Eigenvector
Degree Degree Centrality
Centrality Centrality

C...
Copenhagen Zoo Facebook
Performance
• Largest surge in likes ever
• Almost 100K People
Talking About This (PTAT)
on Facebo...
Interactions (LCS Historical)
Check-ins
• Beforehand, 29K
people added the
Zoo’s location to a
Facebook post
• Now 110K people
“Were Here” on
Facebook
•...
Initial Findings
Overall
• Twitter offered a more direct reflection of events, in terms
of volume and sentiment
• Twitter ...
Danish Comparison
Contrast with Danish Subset
• Mainstream media plays a larger role as
opposed to higher proportions of o...
@socialbeit
@roamingdata
chris@mindjumpers.com
dk.linkedin.com/in/cjzimmerman

follow our projects
Upcoming SlideShare
Loading in …5
×

Project #Marius - Chris Zimmerman, Ravi Vatrapu, Yuran Chen & Dan Hardt

863 views

Published on

Published in: Social Media
  • Be the first to comment

Project #Marius - Chris Zimmerman, Ravi Vatrapu, Yuran Chen & Dan Hardt

  1. 1. Project #Marius Chris Zimmerman Ravi Vatrapu Yuran Chen Dan Hardt
  2. 2. Social Business Intelligence
  3. 3. Real World Reflections
  4. 4. 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)
  5. 5. #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
  6. 6. Visual Analysis & Full Dataset (click here)
  7. 7. 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?
  8. 8. Dataset
  9. 9. Distribution
  10. 10. Distribution (DK)
  11. 11. 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 retweets > 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?
  12. 12. 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.
  13. 13. Timeline of Events (click here)
  14. 14. Social Text
  15. 15. A Sentimental Topic
  16. 16. Radian6 Sentiment-challenged Examples:
  17. 17. Automatic Sentiment Results • Danish data tends to be much more neutral compared to the nonDanish 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?
  18. 18. Language Comparison
  19. 19. #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
  20. 20. Network Analysis
  21. 21. Amplification Influentials #Marius @CopenhagenZoo
  22. 22. Amplified Posts
  23. 23. Centrality Vertex copenhagenzoo InOutBetweenness Closeness Eigenvector Degree Degree Centrality Centrality Centrality Clustering PageRank Coefficient 513 0 767401.779 0.001 0.038 122.346 digitalcake2 1 62 89745.114 0.000 0.004 aprilchristen 27 5 41415.806 0.000 beaumiroir 14 7 39865.026 01bond 38 1 rtenews 37 ebizniz Custom Menu Item Action Tweete d Search Term? 0.002https://twitter.com/copenhagenzoo No 17.351 0.009https://twitter.com/digitalcake2 Yes 0.001 8.279 0.016https://twitter.com/aprilchristen Yes 0.000 0.001 5.353 0.023https://twitter.com/beaumiroir Yes 32366.167 0.000 0.000 9.443 0.000https://twitter.com/01bond Yes 0 28708.167 0.000 0.000 8.838 0.000https://twitter.com/rtenews No 1 9 23755.440 0.000 0.000 3.216 0.000https://twitter.com/ebizniz Yes mmpr_consultant 17 6 19567.043 0.000 0.003 4.570 0.082https://twitter.com/mmpr_consultant Yes earthtransition 12 1 19429.063 0.000 0.000 4.656 0.006https://twitter.com/earthtransition Yes
  24. 24. 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
  25. 25. Interactions (LCS Historical)
  26. 26. Check-ins • Beforehand, 29K people added the Zoo’s location to a Facebook post • Now 110K people “Were Here” on Facebook • CPH Zoo is thus now the 7th most checkedinto place in Denmark
  27. 27. 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).
  28. 28. 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
  29. 29. @socialbeit @roamingdata chris@mindjumpers.com dk.linkedin.com/in/cjzimmerman follow our projects

×