“Calculate Sentiment” function
, 164, 167, 172, 200, 243, 273, 283, 317, 402
Centrality annotations
, 137, 162, 164, 173, 196, 200, 243, 273, 283, 314, 402
Chauhan, Ashok
, 297, 298, 309
Cincinnati Children’s Hospital Medical Center (CCHMC)
, 400
Clinton, Hillary
, 137, 151, 219–228, 350, 356, 365
COINonCOINs community
, 189–190
Collaboration
honest signals of
, 45
balanced contribution
, 49–50
honest language
, 50–51
responsiveness
, 50
rotating leadership
, 49
shared context
, 51–55
strong leadership
, 48
knowledge flow optimization
, 58–61
privacy concerns, dealing with
, 56–58
virtual mirroring
, 56
Collaborative Innovation Networks (COINs)
, 6, 24, 25, 192, 212, 352, 353–354, 386
Collaborative Learning Network (CLN) learning
, 354
Collaborative performance of organizations, measuring
, 419
Communication galaxies, understanding
, 67
Community detection, finding COINs through
, 185–192
Community detection algorithm
, 185, 186, 187, 188
Condor
, 108, 109, 155, 156, 157, 165, 170, 172, 185, 197, 208, 212, 229, 242, 296, 340, 366, 419
analyzing e-mail with
, 108
bipartite graphs, brands through betweenness of actors in
, 136–137
Coolhunting on Internet with
, 11–12
drilling down in
, 394
facebook wall with, analyzing
, 126–129
four-step analysis process. See Four-step analysis process
getting started with
, 121
Google CSE, degree-of-separation search with
, 141–146
graph
, 137
identifying criminals through machine learning in
, 280–290
main parts of
, 113
manual
, 122
sample four-step analysis with twitter
, 130
export
, 134
fetch data
, 130–132
process
, 132
visualize
, 133–134
started with
, 9–10
Twitter, degree-of-separation search with
, 146–150
Wikipedia search
, 150–152
Condor Export Wizards
, 118, 119
Condor software tool
, 3, 28, 57
Conscientiousness
, 103, 244, 253–254, 258
Contribution index
, 49, 70, 74, 75, 154, 204, 215
Contribution index annotations
, 164, 166, 200, 243, 273, 283
Contribution index scatter plot
, 225
Convicts versus nonconvicts
, 287
Coolfarming
, 3, 4, 6, 9, 12, 24, 107, 108
data collection and analysis process
, 31–32
organizations
, 25
through knowledge flow optimization
, 58–61
Coolhunting
, 3, 4, 24, 36, 107, 108, 349
finding trends by finding trendsetter
, 39–44
Francogeddon
, 12, 339–348
on Internet with Condor
, 11–12
on social media
, 40
and trend forecasting on web
, 7, 37
US Presidential elections
, 12
Coolhunting on the Internet with Condor
, 295
analysis of the crowd
, 322–334
expert analysis
, 298–311
swarm analysis
, 311–321
Cooperation, evolution of
, 93
Cooperation and trustworthiness, uncalculating
, 94–95
Correlation results of FFI metrics with six honest signal SNA metrics
, 245–248
Correlations calculation between FFI and e-mail
, 242–244
“Create new dataset”
, 182
Criminal actors, identifying
through their honest signals of collaboration
, 273–280
Criminals, identifying
through machine learning in condor
, 280–290
Crowd
, 296
analysis of
, 322–334
Election outcome, predicting
, 103
Electronic communications
, 3, 28
E-mail
, 2, 25, 65, 115, 242, 393
analyzing with
, 10
calculating personality characteristics from
, 11, 109
predicting criminal intent from
, 11, 109
see also Personality characteristics calculation from e-mail
E-mail analysis with condor
, 153
creating a virtual mirror of an organization
, 192–219
creating virtual mirror of personal e-mailbox
, 154
drawing the term graph
, 172–174
removing the mailbox owner
, 174–185
finding COINs through community detection
, 185–191
Hillary Clinton’s mail, analyzing
, 219–228
organizational aspects of e-mail-based SNA
, 228–231
E-mail-based social network analysis
, 64–65
Enron e-mail archive
, 11, 109, 263
exploratory analysis
, 264–272
identifying criminal actors through their honest signals of collaboration
, 273–280
“tribefinder”
, 280–290
Exchange Autodiscover server
, 157
Extroversion
, 250, 258–259
Online calendars
, 115, 400
Online social media
, 3, 349, 354
Online social network
demographic information, calculating
, 99–103
election outcome, predicting
, 103
facebook, spreading ideas on
, 95–96
financial performance, measuring
, 97–99
ideas spread in
, 8, 85
machine learning, finding fake reviews through
, 96–97
papers covered in section, overview
, 86–88
social selection and peer influence in
, 95
theories of information diffusion
, 89–94
Organizational networks
, 25
Organizational trust and satisfaction, measuring
, 66
Organization’s Communications Patterns assessment
, 32–33
Oscillation annotations
, 164, 165, 200, 243, 273, 283
Outside Media Individual Collaboration (OMIC)
, 13–14, 405–417
annotation process
, 414–417
Outside Media Organizational Collaboration (OMOC)
, 14, 425
annotation process
, 429
Sales effectiveness of a global high-tech company
, 63
Sample course syllabus
, 20–23
Sample mid-term exam
, 465–468
Sanders, Bernie
, 365, 369–376
Script-generated actors
, 197
Shared context
, 51, 53, 54–55
SIC & SOC (Survey of Individual and Organizational Collaboration)
, 14
Six honest signals of collaboration
, 7
Social capital on Facebook
, 96
Social media
, 30
Coolhunting on
, 40
exporters
, 118–120
fetchers
, 115–116
filters
, 116
fundamental analysis
, 108
as quantitative indicator of political behavior
, 103
visualizers
, 116–118
Social network analysis (SNA)
, 5–6, 28
basics of
, 70
E-mail-based
, 64–65
knowledge flow optimization through
, 29–31
and statistics
, 8, 69
Social network picture
of COINs seminar network
, 47
Social networks
, 5, 90
and cooperation in hunter-gatherers
, 91–92
influential and susceptible members of
, 95–96
trend prediction by analyzing
, 6
trend prediction by measuring
, 24
Social Quantum Physics, principles of
, 16
SPSS statistical package
, 114, 120
Statistical techniques
, 8
Statistics
basics of
, 75
linear regression
, 80, 82–83
Pearson correlation
, 78–80, 81
and SNA
, 75
t-test
, 76, 78
Stock market
Twitter mood predicts
, 98
Wikipedia usage patterns
, 98–99
Strong leadership
, 48, 54
Survey of individual collaboration (SIC)
, 431–438
empathy/listening
, 438
fairness
, 435
forgiveness
, 437
organizational motivation
, 433
transparency
, 434
trust/honesty
, 436
Survey of organizational collaboration (SOC)
, 431, 439–443
collective consciousness
, 440
contribution/sharing
, 442
leadership
, 441
responsiveness/respect
, 443
Swarm analysis
, 296, 311–321
Synthetic Minority Over-sampling Technique (SMOTE) algorithm
, 285, 287
Temporal social surface
, 208
“Term graph” function
, 172
Theories of information diffusion
, 89–94
Trend forecasting
, 107, 108
Trends finding by finding trendsetter
, 39–43
“Tribefinder”
, 280–290, 350, 366–382
Trump, Donald
, 350, 365–368, 377–381
Turntaking annotations
, 164, 166, 200, 243, 273, 283
Twitter
, 2, 3, 25, 101, 112, 115, 136, 146–150, 296, 322–334, 425, 427
EgoFetcher
, 414–416
Tribefinder
, 382
2015/2016 Bernie Sanders campaign
, 349
2016 US Presidential elections
, 350
Bernie Sander’s presidential campaign
, 353–355
Coolhunting Bernie Sanders, Hillary Clinton, Jeb Bush, and Donald Trump
, 356–366
tribefinder on twitter
, 366–382