Project #MariusChris Zimmerman

Ravi VatrapuYuran ChenDan Hardt

Social Business Intelligence

Real World Reflections

Overview

1. The Story: Timeline of Online/Offline Events2. Dataset, Tools and Visualization3. Research Angles4. Volume Comparison5. Community Language & Demographics6. Social Graph: Network, Actor Post-Level

Analysis7. Social Text: Language & Sentiment Analysis8. Company Performance9. Initial Findings

Timeline of Events

tinyurl.com/mariustimeline

Research Avenues of InquiryThe 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?

ToolsetTool Purpose Access

Radian6 Collection License

Nitrogram Collection License

Tableau Desktop Visualization / Analysis License (edu)

Datawrapper Visualization Public

TimelineJS Visualization Public

LIWC Language Analysis Public

SODATOCollection / 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

tinyurl.com/mariusvisual

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.

#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

VertexIn-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 0.002https://twitter.com/copenhagenzoo No

digitalcake2 1 62 89745.114 0.000 0.004 17.351 0.009https://twitter.com/digitalcake2 Yes

aprilchristen 27 5 41415.806 0.000 0.001 8.279 0.016https://twitter.com/aprilchristen Yes

beaumiroir 14 7 39865.026 0.000 0.001 5.353 0.023https://twitter.com/beaumiroir Yes

01bond 38 1 32366.167 0.000 0.000 9.443 0.000https://twitter.com/01bond Yes

rtenews 37 0 28708.167 0.000 0.000 8.838 0.000https://twitter.com/rtenews No

ebizniz 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

Social Text

Language Comparison

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?

Sentiment Results

Facebook Fanpage

Copenhagen Zoo FacebookPerformance• 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 Facebook

• 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

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