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, Leeds, 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
- Prelims
- 1 Introduction
- Part I Trend Prediction by Measuring Social Networks
- 2 Coolfarming Organizations
- 3 Coolhunting and Trend Forecasting on the Web
- 4 The Six Honest Signals of Collaboration
- 5 Essentials of Social Network Analysis and Statistics
- 6 How Ideas Spread in Online Social Networks — Readings
- Part II Analyzing Structure, Dynamics, and Content of Networks with Condor
- 7 The Four-Step Analysis Process
- 8 Getting Started with Condor
- 9 Analyzing E-Mail with Condor
- 10 Calculating Personality Characteristics from E-Mail
- 11 Predicting Criminal Intent from E-Mail — Analyzing the Enron E-Mail Archive
- 12 Coolhunting on the Internet with Condor
- 13 Coolhunting — Francogeddon
- 14 Coolhunting the US Presidential Elections
- Part III Automatic Media Insights Coin Assessment (AMICA)
- 15 Inside Media Individual Collaboration (IMIC)
- 16 Outside Media Individual Collaboration (OMIC)
- 17 Inside Media Organizational Collaboration (IMOC)
- 18 Outside Media Organizational Collaboration (OMOC)
- 19 Survey of Individual and Organizational Collaboration (SIC & SOC)
- Part IV Appendix — Useful Machine Learning and Graph Analysis Tools
- Appendix A Identifying Anti-Vaxxers through Machine Learning Using KNIME
- Appendix B Generating Nice Graph Pictures with Gephi
- Appendix C Sample Mid-Term Exam
- Appendix D References
- Biography
- Index