Index

Peter A. Gloor (MIT Center for Collective Intelligence, Massachusetts Institute of Technology, Cambridge, MA, USA)

Sociometrics and Human Relationships

ISBN: 978-1-78714-113-1, eISBN: 978-1-78714-112-4

Publication date: 29 April 2017

This content is currently only available as a PDF

Citation

Gloor, P.A. (2017), "Index", Sociometrics and Human Relationships, Emerald Publishing Limited, Bingley, pp. 485-493. https://doi.org/10.1108/978-1-78714-112-420171030

Publisher

:

Emerald Publishing Limited

Copyright © 2017 Peter A. Gloor


INDEX

Actor filter
, 163, 189

Actors, in SNA
, 70

Actor scatter plot
, 133, 167, 179

Adjusted R Square
, 249, 250, 258, 259

Agreeability
, 250, 259–260

“Allteams-cleaned”
, 200

Amity University, India
, 297–298, 300, 311, 312, 316–317, 322

Annotate functions
, 164, 243

ANOVA results

by ethnicity for FFI characteristics
, 256

by gender for FFI characteristics
, 255

by nationality for FFI characteristics
, 257

Anti-gaming
, 66

Anti-vaccination
, 447

Antivaxxers identification through machine learning
, 447–457

Asteroid belt
, 160, 183

Automatic Media Insights COIN Assessment (AMICA)
, 4, 13, 17, 385–389

Average Response Time (ART)
, 154, 345, 346, 403

Balanced contribution
, 49–50, 52

BeingExample
, 334

Bernie Sander’s presidential campaign
, 352, 353–355

Betweenness centrality
, 70, 72–73, 188, 306, 308

Betweenness curves
, 178

Bidirectional links
, 150, 312–313, 315

Bipartite graphs

measuring the importance of brands through betweenness of actors in
, 136–137

Black swans
, 108

Blogs
, 3, 298–311

Bowling for fascism
, 90–91

Brands, calculating the importance of
, 305

“Brothers”
, 333

Bush, Jeb
, 356, 360, 361, 363, 364

“Calculate Sentiment” function
, 164, 167, 172, 200, 243, 273, 283, 317, 402

Calendar data
, 2

Centrality annotations
, 137, 162, 164, 173, 196, 200, 243, 273, 283, 314, 402

Chat
, 3, 4

Chauhan, Ashok
, 297, 298, 309

Cincinnati Children’s Hospital Medical Center (CCHMC)
, 400

Classic SNA
, 28

Clinton, Hillary
, 137, 151, 219–228, 350, 356, 365

Clustered network
, 89–90

COIIN project
, 184

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
, 78–80, 81

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

Creativity
, 65–66

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

CSV data
, 220

Deceptive opinion spam, finding
, 96–97

Degree centrality
, 70, 72, 73, 137, 181

Demographic information

calculating
, 99–103

extracting
, 85, 86

Density
, 70, 74, 186

Directed graph
, 71

Edges
, 70

EgoFetcher
, 414–416

Ego networks
, 25, 192

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

Emails.csv
, 220

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

Expert analysis
, 298–311

Experts
, 296

Exporters
, 113, 118–120

Extroversion
, 250, 258–259

Facebook
, 3, 25, 112, 115, 425

spreading ideas on
, 95–96

Facebook wall, analyzing
, 126–129

Face-to-face communication
, 3, 30, 38

FeelTheBern.com
, 352

Fetch content
, 157

Fetchers
, 111, 112, 113, 115–116

“Fetch Web”
, 299

Filters
, 112, 113, 116

Financial capital, improving

through optimizing social capital
, 65–67

Financial performance, measuring
, 97–99

Four-step analysis process
, 111

social media
, 111

exporters
, 118–120

fetchers
, 115–116

filters
, 116

visualizers
, 116–118

Francogeddon
, 339–348

Gates, Bill
, 408, 409–410

Geotagging
, 296

Gephi, generating graph pictures with
, 15, 459–464

GMAIL login dialog
, 158, 159

GMAIL mailbox
, 194

Google
, 43, 93, 297, 425, 427

Google Custom Search
, 115

Google Custom Search Engine (CSE)
, 136

degree-of-separation search with
, 141–146

Google Trends
, 97, 350

Graph
, 28, 137–140

Grexit
, 342

Group betweenness centrality
, 70, 74, 118, 345

Group degree centrality
, 70, 73

Happiness paradox
, 101

Hawthorne effect
, 56

Hillary Clinton’s mail, analyzing
, 219–228

Homophily, evolution of
, 94

Honest language
, 50–51, 53, 61

Huffington, Arianna
, 408

Huffington Post
, 352

IIT
, 298, 320–321

IMAP account
, 158

“Import local data first”
, 212

Infant Mortality reduction Collaboration Improvement and Innovation Networks (IM CoIIN)
, 189, 400

Inside media individual collaboration (IMIC)
, 13, 391–403

annotation process
, 401–403

Inside media organizational collaboration (IMOC)
, 14, 419–423

annotation process
, 423

Internet
, 38, 92–93, 264, 295–334

Kaggle website
, 220

KNIME
, 447–458

environment
, 8

identifying anti-vaxxers through machine learning using
, 15

Knowledge flow optimization
, 58–61

analyze
, 59

coolfarming
, 58

mirror
, 60–61

optimize
, 61

through organizational social network analysis
, 29–31

predict
, 59

Known unknowns
, 107–108

Krugman, Paul
, 408

Libertea2012
, 352

Linear regression
, 80, 82–83

“Load actor merge CSV”
, 198

Louvain algorithm
, 185–186

Machine learning
, 447–458

finding fake reviews through
, 96–97

Mailbox owner, removing
, 174–185

Mann-Whitney U-test
, 345

“Manual node merging” wizard
, 161, 186

Matlab
, 120

Microsoft
, 427

MIT
, 46, 298, 320–321

MSFTExchange
, 427

MySQL
, 115, 122, 124, 155, 156, 326, 461

Natural language processing (NLP)
, 212

Neo-FFI test
, 242

Neuroticism
, 103, 244, 249

Nick_Ksg
, 334

“Node labels”
, 307

Nodes
, 70

“Nonconvicts”
, 287

Nudges
, 50, 345

One-semester course
, 18

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

Openness
, 250

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

Pearson correlation
, 78–80, 81

Performance metrics

correlating communication patterns against
, 34

Personal e-mailbox analysis
, 154

creating virtual mirror of personal e-mailbox
, 154

drawing the term graph
, 172–185

removing the mailbox owner
, 174–185

Personality and word use among bloggers
, 102–103

Personality characteristics calculation from e-mail
, 241

adding gender, ethnicity, and nationality as control variables
, 254–260

agreeability
, 259–260

extroversion
, 258–259

calculating correlations between FFI and e-mail
, 242–244

general prediction formula, developing
, 244

agreeability
, 250

conscientiousness
, 253–254

extroversion
, 250

neuroticism
, 244

openness
, 250

Persons.csv file
, 220

Privacy concerns, dealing with
, 56–58

Problem
, 170

Process Dataset
, 154

Pro-vaxxers
, 448

R, statistical package
, 120

Receiver operating characteristics (ROC) curve
, 288

Reddit
, 352, 353

Regression
, 80, 82–83

Regression coefficients for regressing six honest signals

against agreeability
, 260

against agreeability with ethnicity as control variable
, 260

against conscientiousness
, 253–254

against extraversion
, 251

against extraversion with ethnicity as control variable
, 259

against neuroticism
, 249

against openness
, 252

“Remove specific actor” function
, 175, 188

Responsiveness
, 50, 52

RFSchatten
, 352

Rotating leadership
, 49, 52

Sales effectiveness of a global high-tech company
, 63

Sample course syllabus
, 20–23

Sample download
, 444

Sample mid-term exam
, 465–468

Sanders, Bernie
, 365, 369–376

Script-generated actors
, 197

Shantrjosh
, 427

Shared context
, 51, 53, 54–55

SIC & SOC (Survey of Individual and Organizational Collaboration)
, 14

Six honest signals of collaboration
, 7

6670G
, 334

Skype
, 2, 115, 393

Slander
, 427

SMOTE
, 373, 378

“Snowball sampling”
, 230

Social capital on Facebook
, 96

Social fMRI
, 102

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

Spammers
, 66

SPSS statistical package
, 114, 120

SPSS’ t-test
, 274, 276

SQLite database
, 220

Stata
, 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

Strong ties
, 89

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

Swiss Franc
, 340, 342

Swiss National Bank
, 340

Synthetic Minority Over-sampling Technique (SMOTE) algorithm
, 285, 287

Tag cloud, creating
, 223

Temporal social surface
, 208

“Term graph” function
, 172

“Terms”
, 172

Theories of information diffusion
, 89–94

Ties
, 70

Trend forecasting
, 107, 108

Trends finding by finding trendsetter
, 39–43

“Tribefinder”
, 280–290, 350, 366–382

Trump, Donald
, 350, 365–368, 377–381

t-test
, 76, 78, 274, 276

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

Undirected network
, 70

Unidirectional links
, 313

Unknown unknowns
, 108

Videoconferencing
, 3

Virtual collaboration projects
, 193

Virtual mirror creation of an organization
, 192–219

Virtual mirroring
, 32, 34–36, 56, 107, 108

Virtual tribes
, 366, 368–369

Visualizers
, 113, 116, 118

Weak ties
, 89

Web
, 295

Websites and blogs
, 298–311

Wiki Evolution Fetcher
, 311, 318

Wikipedia
, 2, 3, 42, 93, 112, 115, 136, 150–152, 311–321, 425

controversial topics in
, 99–100

“With history” option
, 177, 207

Word Cloud
, 154